Reviewerly/exHarmony huggingface dataset:
Aci-bench: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation

Abstract Recent immense breakthroughs in generative models such as in GPT4 have precipitated re-imagined ubiquitous usage of these models in all applications. One area that can benefit by improvements in artificial intelligence (AI) is healthcare. The note generation task from doctor-patient encounters, and its associated electronic medical record documentation, is one of the most arduous time-consuming tasks for physicians. It is also a natural prime potential beneficiary to advances in generative models. However with such advances, benchmarking is more critical than ever. Whether studying model weaknesses or developing new evaluation metrics, shared open datasets are an imperative part of understanding the current state-of-the-art. Unfortunately as clinic encounter conversations are not routinely recorded and are difficult to ethically share due to patient confidentiality, there are no sufficiently large clinic dialogue-note datasets to benchmark this task. Here we present the Ambient Clinical Intelligence Benchmark ( aci-bench ) corpus, the largest dataset to date tackling the problem of AI-assisted note generation from visit dialogue. We also present the benchmark performances of several common state-of-the-art approaches.
An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters

Medical doctors spend on average 52 to 102 minutes per day writing clinical notes from their patient encounters (Hripcsak et al., 2011). Reducing this workload calls for relevant and efficient summarization methods. In this paper, we introduce new resources and empirical investigations for the automatic summarization of doctor-patient conversations in a clinical setting. In particular, we introduce the MTS-Dialog dataset; a new collection of 1,700 doctor-patient dialogues and corresponding clinical notes. We use this new dataset to investigate the feasibility of this task and the relevance of existing language models, data augmentation, and guided summarization techniques. We compare standard evaluation metrics based on n-gram matching, contextual embeddings, and Fact Extraction to assess the accuracy and the factual consistency of the generated summaries. To ground these results, we perform an expert-based evaluation using relevant natural language generation criteria and task-specific criteria such as critical omissions, and study the correlation between the automatic metrics and expert judgments. To the best of our knowledge, this study is the first attempt to introduce an open dataset of doctor-patient conversations and clinical notes, with detailed automated and manual evaluations of clinical note generation.
The NLP Task Effectiveness of Long-Range Transformers

Transformer models cannot easily scale to long sequences due to their O(Nˆ2) time and space complexity. This has led to Transformer variants seeking to lower computational complexity, such as Longformer and Performer. While such models have theoretically greater efficiency, their effectiveness on real NLP tasks has not been well studied. We benchmark 7 variants of Transformer models on 5 difficult NLP tasks and 7 datasets. We design experiments to isolate the effect of pretraining and hyperparameter settings, to focus on their capacity for long-range attention. Moreover, we present various methods to investigate attention behaviors to illuminate model details beyond metric scores. We find that the modified attention in long-range transformers has advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error.
Conversational assistants in academic libraries: enhancing reference services through Bing Chat

Purpose This research aims to explore the integration of Bing Chat, an artificial intelligence (AI)-powered conversational assistant, into academic libraries and its potential impact on reference services. Design/methodology/approach This study analyzes existing literature to investigate the integration of Bing Chat in academic libraries. This research focuses on its features, capabilities and implications for enhancing user experiences and addressing information needs. Findings The findings highlight that Bing Chat’s conversational interface facilitates more dynamic and context-aware reference interactions, fostering user empowerment and personalized learning pathways. The integration of Bing Chat with existing library resources enhances resource discovery, navigational assistance and engagement with visual content. However, challenges related to accuracy, privacy and algorithmic bias require careful consideration. Originality/value This research contributes to the literature by providing an in-depth exploration of Bing Chat’s integration into academic libraries, emphasizing its transformative potential for reference services. This study addresses ethical and social dimensions associated with AI-driven tools in library environments, offering insights into ensuring fairness, privacy and user trust.
Improved YOLOv5-based for small traffic sign detection under complex weather

Abstract Traffic sign detection is a challenging task for unmanned driving systems. In the traffic sign detection process, the object size and weather conditions vary widely, which will have a certain impact on the detection accuracy. In order to solve the problem of balanced detecting precision of traffic sign recognition model in different weather conditions, and it is difficult to detect occluded objects and small objects, this paper proposes a small object detection algorithm based on improved YOLOv5s in complex weather. First, we add the coordinate attention(CA) mechanism in the backbone, a light-weight yet effective module, embedding the location information of traffic signs into the channel attention to improve the feature extraction ability of the network. Second, we exploit effectively fine-grained features about small traffic signs from the shallower layers by adding one prediction head to YOLOv5s. Finally, we use Alpha-IoU to improve the original positioning loss CIoU, improving the accuracy of bbox regression. Applying this model to the recently proposed CCTSDB 2021 dataset, for small objects, the precision is 88.1%, and the recall rate is 79.8%, compared with the original YOLOv5s model, it is improved by 12.5% and 23.9% respectively, and small traffic signs can be effectively detected under different weather conditions, with low miss rate and high detection accuracy. The source code will be made publicly available at https://github.com/yang-0706/ImprovedYOLOv5s .
Efficient optimization with higher-order Ising machines

A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability problems, map more seamlessly to Ising networks with higher-order interactions. Here, we demonstrate that higher-order Ising machines can solve satisfiability problems more resource-efficiently in terms of the number of spin variables and their connections when compared to traditional second-order Ising machines. Further, our results show on a benchmark dataset of Boolean k-satisfiability problems that higher-order Ising machines implemented with coupled oscillators rapidly find solutions that are better than second-order Ising machines, thus, improving the current state-of-the-art for Ising machines.
Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation

Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA) aims to significantly improve the classification performance and generalization capability of the model by leveraging the presence of a small amount of labeled data from the target domain. Several SSDA approaches have been developed to enable semantic-aligned feature confusion between labeled (or pseudo-labeled) samples across domains; nevertheless, owing to the scarcity of semantic label information of the target domain, they were arduous to fully realize their potential. In this study, we propose a novel SSDA approach named Graph-based Adaptive Betweenness Clustering (G-ABC) for achieving categorical domain alignment, which enables cross-domain semantic alignment by mandating semantic transfer from labeled data of both the source and target domains to unlabeled target samples. In particular, a heterogeneous graph is initially constructed to reflect the pairwise relationships between labeled samples from both domains and unlabeled ones of the target domain. Then, to degrade the noisy connectivity in the graph, connectivity refinement is conducted by introducing two strategies, namely Confidence Uncertainty based Node Removal and Prediction Dissimilarity based Edge Pruning. Once the graph has been refined, Adaptive Betweenness Clustering is introduced to facilitate semantic transfer by using across-domain betweenness clustering and within-domain betweenness clustering, thereby propagating semantic label information from labeled samples across domains to unlabeled target data. Extensive experiments on three standard benchmark datasets, namely DomainNet, Office-Home, and Office-31, indicated that our method outperforms previous state-of-the-art SSDA approaches, demonstrating the superiority of the proposed G-ABC algorithm.
Text Classification via Large Language Models

Despite the remarkable success of large-scale Language Models (LLMs) such as GPT-3, their performances still significantly underperform fine-tuned models in the task of text classification.This is due to (1) the lack of reasoning ability in addressing complex linguistic phenomena (e.g., intensification, contrast, irony etc); (2) limited number of tokens allowed in in-context learning. In this paper, we introduce Clue And Reasoning Prompting (CARP). CARP adopts a progressive reasoning strategy tailored to addressing the complex linguistic phenomena involved in text classification: CARP first prompts LLMs to find superficial clues (e.g., keywords, tones, semantic relations, references, etc), based on which a diagnostic reasoning process is induced for final decisions. To further address the limited-token issue, CARP uses a fine-tuned model on the supervised dataset for kNN demonstration search in the in-context learning, allowing the model to take the advantage of both LLM’s generalization ability and the task-specific evidence provided by the full labeled dataset. Remarkably, CARP yields new SOTA performances on 4 out of 5 widely-used text-classification benchmarks, 97.39 (+1.24) on SST-2, 96.40 (+0.72) on AGNews, 98.78 (+0.25) on R8 and 96.95 (+0.6) on R52, and a performance comparable to SOTA on MR (92.39 v.s. 93.3). More importantly, we find that CARP delivers impressive abilities on low-resource and domain-adaptation setups. Specifically, using 16 examples per class, CARP achieves comparable performances to supervised models with 1,024 examples per class.
Filling the SME credit gap: a systematic review of blockchain-based SME finance literature

Purpose The review examines the existing literature on blockchain-based small and medium enterprise (SME) finance and highlights its trend, themes, opportunities and challenges. Based on these factors, the authors create a framework for the existing literature on blockchain-based SME financing and lay down future research paths. Design/methodology/approach The review follows a systematic approach. It includes 53 articles encompassing multiple dimensions of blockchain-based SME finance, including peer-to-peer lending platforms, supply chain finance (SCF), decentralized lending protocols and tokenization of assets. The review critically evaluates these approaches' theoretical underpinnings, empirical evidence and practical implementations. Findings The review demonstrates that blockchain-based SME finance holds significant promise in addressing the credit gap by leveraging blockchain technology's decentralized and transparent nature. Benefits identified include reduced information asymmetry, improved access to financing, enhanced credit assessment processes and increased financial inclusion. However, the literature acknowledges several challenges and limitations, such as regulatory uncertainties, scalability issues, operational complexities and potential security risks. Originality/value The article contributes to the growing knowledge of blockchain-based SME finance by synthesizing and evaluating the existing literature. It also provides a framework for the existing literature in the area and future research paths. The study offers insights for researchers, policymakers and practitioners seeking to understand the potential of blockchain technology in filling the SME credit gap and fostering economic development through improved access to finance for SMEs.
Towards a Unified Architecture of Knowledge Management System for a Research Institute

This paper presents some elements of architecture of planned knowledge management system dedicated to research institutions. Main contributions include social extension of the idea of adaptive hermeneutic agent and preliminary implementation of domain specific language for development of knowledge management systems. Work described here concentrated on practical verification of viability of proposed ideas and took form of a prototype software system, which can be used by a group of researchers to easily find and recommend relevant information
Efficient Nonprofiling 2nd-Order Power Analysis on Masked Devices Utilizing Multiple Leakage Points

2nd-order attacks utilize power values at two leakage points to break cryptographic systems protected by 1st-order random masking. Without profiling, the attacker do not know the exact location of the two leakage points. Standard 2nd-order attacks with an exhaustive search over two windows of size nw has computational complexity O(n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and does not scale well with the window size nw. We propose to apply a decision-combination attack, the majority vote (MV) attack, to combine 2nd order attacks at multiple candidate pairs of leakage points selected through two filters. The first filter pre-process the power traces with Fast Fourier Transformation (FFT) techniques and reduce the complexity to O(n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> )). The second filter use an advanced statistical feature selection procedure, Higher Criticism (HC), to select leakage candidates that improve the effectiveness of decision-combination MV attack and other leakage-combination attacks. We derive theoretical success conditions of MV attacks as well as the typical maximum attack and a leakage-combination sum attack. The theoretical conditions are confirmed through performance comparisons of the attacks on synthetic data sets and on two real data sets, an FPGA implementation and a software implementation of masked AES. The proposed FF-HC-MV attack is data-adaptive, working well in all data sets.
Authorship Verification in Arabic Using Function Words: A Controversial Case Study of Imam Ali's Book <i>Peak of Eloquence</i>

This paper addresses the viability of two multivariate methods (Principal Components Analysis and Cluster Analysis) in verifying the disputed authorship of a famous Arabic religious book called (Nahjul-Balagha/ Peak of Eloquence). This book occupies an exceptional position in the history of the huge debates held between the two basic Islamic sectors: Sunni'e and Shia. Therefore, it represents a serious challenge to the viability of the multivariate techniques in resolving certain types of historical and sectarian conflicts and controversies. Furthermore, verifying the authorship of this book could be a good opportunity to find out whether there are certain quantitative techniques of attribution that hold for different languages such as English and Arabic. Function words have been targeted in this paper as possible indicators of the author's identity. Accordingly, a set of Arabic function words would be tested using WordSmith Tools (version 5). It turned out that the multivariate techniques are most likely robust for addressing the type of issues raised about Nahjul- Balagha. Besides, it appeared that the statistical patterns of function word usages are quite sensitive to genre in Arabic.
Research on the Architecture and its Implementation for Instrumentation and Measurement Cloud

Cloud computing has brought a new method of resource utilization and management. Nowadays some researchers are working on cloud-based instrumentation and measurement systems designated as Instrumentation and Measurement Clouds (IMCs). However, until now, no standard definition or detailed architecture with an implemented system for IMC has been presented. This paper adopts the philosophy of cloud computing and brings forward a relatively standard definition and a novel architecture for IMC. The architecture inherits many key features of cloud computing, such as service provision on demand, scalability and so on, for remote Instrumentation and Measurement (IM) resource utilization and management. In the architecture, instruments and sensors are virtualized into abstracted resources, and commonly used IM functions are wrapped into services. Users can use these resources and services on demand remotely. Platforms implemented under such architecture can reduce the investment for building IM systems greatly, enable remote sharing of IM resources, increase utilization efficiency of various resources, and facilitate processing and analyzing of Big Data from instruments and sensors. Practical systems with a typical application are implemented upon the architecture. Results demonstrate that the novel IMC architecture can provide a new effective and efficient framework for establishing IM systems.
Making Neighbors Quiet: An Approach to Detect Virtual Resource Contention

It is imperative for public cloud providers to guarantee performance targets for tenants' virtual machines (VMs) while respecting strict business confidentiality, e.g., having no information on applications nor their performance. A large body of related work addresses the challenges of detecting performance interferences by leveraging client's quality of service (QoS) metrics, e.g., latency, and additional profiling servers. In this paper, we take the perspective of the cloud provider and propose a general black-box approach that detects different resource contentions by throttling neighboring VMs. Specifically, we design a three-phase detection algorithm that includes: (i) an alarm phase to identify statistical outliers using control charts; (ii) a passive clustering phase to match the current sample to historical behaviors; and (iii) an active throttling phase to discern contentions from application phase changes via throttling. The algorithm is specifically designed for scenarios where multiple co-located VMs request detection analysis simultaneously. We implement and evaluate the proposed three-phase algorithm on four latency sensitive applications, i.e., Wikimedia and three benchmarks from Cloudsuite. Our extensive experimental results show that we can reach an average detection accuracy above 90 percent while limiting the performance degradation experienced by offender workloads to short learning phases.
Providing Service Continuity in Clouds Under Power Outage

In cloud computing, it is crucial to maintain service continuity, while power outage is one of the most common and serious threats. To improve the resilience of cloud against power outage, a service provider usually deploys emergency energy supply (e.g., UPSs and generators) in a data center. When a power outage at a data center happens, the cloud service provider needs to make the operation decision on which subset of VMs to keep running and which servers to host such VMs to minimize its loss (or maximize its profit) using the emergency energy supply while the selected VMs are running in the affected data center until they are finished, migrated to other data centers, or normal power supply of the affected data center has been restored. No prior research has theoretically studied such a cloud service continuity problem under power outage. In this paper, we tackle this challenge and investigate the cloud service continuity problem. Specifically, we consider that a profit is associated with maintaining the continuity of a service, denoted as service continuity profit. Based on that we first formulate an optimization problem that aims to maximize the total profit subject to energy constrains. After showing the hardness of the problem, we focus on the design of approximation algorithms for solving the problem, where we consider two practical cases. In the first one with sufficient number of servers for re-provisioning, we develop a constant approximation algorithm of which the worst-case performance approaches the optimal solution within a constant factor (≈4.5-6.4). In the second one, we consider the general case with limited number of servers, and we develop an approximation algorithm with an approximation ratio of around 5.7-8. By combining these two algorithms together, we can achieve both good worst-case performance and average performance. Simulation results demonstrate the efficiency in terms of maximizing the service continuity profit of the proposed algorithms.
Minimizing Data Access Latencies for Virtual Machine Assignment in Cloud Systems

Cloud systems empower the big data management by providing virtual machines (VMs) to process data nodes (DNs) in a faster, cheaper and more effective way. The efficiency of a VM allocation is an important concern that is influenced by the communication latencies. In the literature, it has been proved that the VM assignment minimizing communication latency in the presence of the triangle inequality is 2-approximation. However, a 2-approximation solution is not efficient enough as data center networks are not limited to the triangle inequality. In this paper, we define the quadrilateral inequality property for latencies such that the time complexity of the VM assignment problem minimizing communication latency in the presence of the quadrilateral inequality is in P (polynomial) class. Indeed, we propose an algorithm for the problem of assigning VMs to DNs to minimize the maximum latency among allocated VMs in addition to DNs with their assigned VMs. This algorithm is latency optimal and 2-approximation for networks with the quadrilateral inequality and the triangle inequality, respectively. Besides, the extension of the proposed method can be applied to the cloud elasticity. The simulation results illustrate the good performance and scalability of our method in various known data center networks.