Reviewerly/exHarmony huggingface dataset:
Large Language Models and Generative AI, Oh My!

Overview We have all read the headlines heralding, often hyperbolically, the latest advances in text- and image-based Artificial Intelligence (AI). What is perhaps most unique about these developments is that they now make relatively good AI accessible to the average Internet user. These new services respond to human prompts, written in natural language, with generated output that appears to satisfy the prompt. Consequently, they are categorized under the term “generative AI,” whether they are generating text, images, or other media. They work by modeling human language statistically, to “learn” patterns from extremely large datasets of human-created content, with those that specifically focus on text therefore called Large Language Models (LLMs). As we have all tried products such as ChatGPT or Midjourney over the past year, we have undoubtedly begun to wonder how and when they might impact our archaeological work. Here, I review the state of this type of AI and the current challenges with using it meaningfully, and I consider its potential for archaeologists.
The Role of Blockchain Technology in Increasing Economic Transparency and Public Trust

Blockchain technology has become one of the most promising technological innovations in recent years. The main advantages of blockchain technology are high transparency, security, and data integrity. Therefore, this technology has attracted attention in various sectors, including the economy. This research aims to examine the role of blockchain technology in enhancing economic transparency and public trust. This study is a literature review that adopts a qualitative method, which means it will analyse and interpret data by utilising information and texts derived from various sources. The results of the study show that blockchain technology has proven itself to be a highly effective tool for achieving this goal. In an increasingly connected and data-dependent society, economic transparency and public trust are key factors in sustainable economic growth and social stability.
Precision medicine for cardiometabolic disease: a framework for clinical translation

Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.
Image Compression Techniques: Literature Review

With the development of modern communications technology, data compression is becoming more important to save space and reduce transmission costs. Because of this, various types and strategies of image compression were provided by several researchers, some of these studies were discussed in this review. The two main types of image compression are Lossless and lossy compression, with many methods for each of them. This research also described various lossless and lossy compression algorithms that were used by many researchers studied that reported in this literature. Lastly, certain conclusions have been provided based on the results of the conducted survey.
A Forest Wildlife Detection Algorithm Based on Improved YOLOv5s

A forest wildlife detection algorithm based on an improved YOLOv5s network model is proposed to advance forest wildlife monitoring and improve detection accuracy in complex forest environments. This research utilizes a data set from the Hunan Hupingshan National Nature Reserve in China, to which data augmentation and expansion methods are applied to extensively train the proposed model. To enhance the feature extraction ability of the proposed model, a weighted channel stitching method based on channel attention is introduced. The Swin Transformer module is combined with a CNN network to add a Self-Attention mechanism, thus improving the perceptual field for feature extraction. Furthermore, a new loss function (DIOU_Loss) and an adaptive class suppression loss (L_BCE) are adopted to accelerate the model’s convergence speed, reduce false detections in confusing categories, and increase its accuracy. When comparing our improved algorithm with the original YOLOv5s network model under the same experimental conditions and data set, significant improvements are observed, in particular, the mean average precision (mAP) is increased from 72.6% to 89.4%, comprising an accuracy improvement of 16.8%. Our improved algorithm also outperforms popular target detection algorithms, including YOLOv5s, YOLOv3, RetinaNet, and Faster-RCNN. Our proposed improvement measures can well address the challenges posed by the low contrast between background and targets, as well as occlusion and overlap, in forest wildlife images captured by trap cameras. These measures provide practical solutions for enhanced forest wildlife protection and facilitate efficient data acquisition.
A Conceptual Digital Forensic Investigation Model Applicable to the Drone Forensics Field

Although there is a considerable amount of studies in drone forensics that describe numerous practical and technical perspectives, there is a lack of a comprehensive investigation framework. This study used design science research methodology to design a conceptual model for the comprehensive investigation of Unmanned Aerial Vehicles (UAVs) under forensic conditions. This model can identify, capture, preserve, analyze, and document UAV incidents. The proposed model consists of four stages: preparation, data collection, analysis, and documentation. In the preparation stage, data are collected and analyzed about UAV-related resources, including the origin and model of the aircraft, any software or hardware installed onboard, and the legal framework and regulations in place. The data collection stage involves the completion of the collection process, where participants gather parts of the UAV and the data needed, such as the flight controller, flight log, and memory cards. The analysis stage involves analyzing the collected evidence. Lastly, the documentation stage involves documenting relevant evidence, analysis results, and any conclusions derived. This model provides a comprehensive process to forensically investigate UAV incidents and provides an efficient and effective approach to the analysis of UAV evidence, ensuring that evidence was collected and analyzed according to accepted forensic techniques. The proposed model can be applied to any UAV type and legal framework.
Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text

The recent advances in large language models (LLM) and foundation models with emergent capabilities have been shown to improve the performance of many NLP tasks. LLMs and Knowledge Graphs (KG) can complement each other such that LLMs can be used for KG construction or completion while existing KGs can be used for different tasks such as making LLM outputs explainable or fact-checking in Neuro-Symbolic manner. In this paper, we present Text2KGBench, a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology. Given an input ontology and a set of sentences, the task is to extract facts from the text while complying with the given ontology (concepts, relations, domain/range constraints) and being faithful to the input sentences. We provide two datasets (i) Wikidata-TekGen with 10 ontologies and 13,474 sentences and (ii) DBpedia-WebNLG with 19 ontologies and 4,860 sentences. We define seven evaluation metrics to measure fact extraction performance, ontology conformance, and hallucinations by LLMs. Furthermore, we provide results for two baseline models, Vicuna-13B and Alpaca-LoRA-13B using automatic prompt generation from test cases. The baseline results show that there is room for improvement using both Semantic Web and Natural Language Processing techniques. Resource Type: Evaluation Benchmark Source Repo: https://github.com/cenguix/Text2KGBench DOI: https://doi.org/10.5281/zenodo.7916716 License: Creative Commons Attribution (CC BY 4.0)
MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval

Abstract Motivation Information retrieval (IR) is essential in biomedical knowledge acquisition and clinical decision support. While recent progress has shown that language model encoders perform better semantic retrieval, training such models requires abundant query–article annotations that are difficult to obtain in biomedicine. As a result, most biomedical IR systems only conduct lexical matching. In response, we introduce MedCPT, a first-of-its-kind Contrastively Pre-trained Transformer model for zero-shot semantic IR in biomedicine. Results To train MedCPT, we collected an unprecedented scale of 255 million user click logs from PubMed. With such data, we use contrastive learning to train a pair of closely integrated retriever and re-ranker. Experimental results show that MedCPT sets new state-of-the-art performance on six biomedical IR tasks, outperforming various baselines including much larger models, such as GPT-3-sized cpt-text-XL. In addition, MedCPT also generates better biomedical article and sentence representations for semantic evaluations. As such, MedCPT can be readily applied to various real-world biomedical IR tasks. Availability and implementation The MedCPT code and model are available at https://github.com/ncbi/MedCPT.
Meta-Learned Models of Cognition

Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. While the former requires the specification of a fixed set of computational structures and a definition of how these structures interact with each other, the latter necessitates the commitment to a particular prior and a likelihood function which - in combination with Bayes' rule - determine the model's behavior. In recent years, a new framework has established itself as a promising tool for building models of human cognition: the framework of meta-learning. In contrast to the previously mentioned model classes, meta-learned models acquire their inductive biases from experience, i.e., by repeatedly interacting with an environment. However, a coherent research program around meta-learned models of cognition is still missing to this day. The purpose of this article is to synthesize previous work in this field and establish such a research program. We accomplish this by pointing out that meta-learning can be used to construct Bayes-optimal learning algorithms, allowing us to draw strong connections to the rational analysis of cognition. We then discuss several advantages of the meta-learning framework over traditional methods and reexamine prior work in the context of these new insights.
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

We present Video-LLaMA, a multi-modal framework that empowers Large Language Models (LLMs) with the capability of understanding both visual and auditory content in the video. Video-LLaMA bootstraps cross-modal training from the frozen pre-trained visual & audio encoders and the frozen LLMs. Unlike previous works that complement LLMs to process the visual or audio signals only, Video-LLaMA enables video comprehension by tackling two challenges: (1) capturing the temporal changes in visual scenes, (2) integrating audio-visual signals. To counter the first challenge, we propose a Video Q-former to assemble a pre-trained image encoder into our video encoder and introduce a video-to-text generation task to learn video-language correspondence. For the second challenge, we leverage ImageBind, a universal embedding model aligning multiple modalities, as the pre-trained audio encoder and introduce an Audio Q-former on top of ImageBind to learn reasonable auditory query embeddings for the LLM module. To align the output of both visual & audio encoders with LLM’s embedding space, we first train Video-LLaMA on massive video/image-caption pairs and then tune our model with visual-instruction datasets of moderate amount but higher quality. We found Video-LLaMA shows the ability to perceive and comprehend video content and generate meaningful responses grounded in the visual and auditory information presented in the videos.
FESTA: Fast Encryption from Supersingular Torsion Attacks

We introduce FESTA, an efficient isogeny-based public-key encryption (PKE) protocol based on a constructive application of the SIDH attacks. At its core, FESTA is based on a novel trapdoor function, which uses an improved version of the techniques proposed in the SIDH attacks to develop a trapdoor mechanism. Using standard transformations, we construct an efficient PKE that is IND-CCA secure in the QROM. Additionally, using a different transformation, we obtain the first isogeny-based PKE that is IND-CCA secure in the standard model. Lastly, we propose a method to efficiently find parameters for FESTA, and we develop a proof-of-concept implementation of the protocol. We expect FESTA to offer practical performance that is competitive with existing isogeny-based constructions.
PhysDiff: Physics-Guided Human Motion Diffusion Model

Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and limits their real-world application. To address this issue, we present a novel physics-guided motion diffusion model (PhysDiff), which incorporates physical constraints into the diffusion process. Specifically, we propose a physics-based motion projection module that uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically-plausible motion. The projected motion is further used in the next diffusion step to guide the denoising diffusion process. Intuitively, the use of physics in our model iteratively pulls the motion toward a physically-plausible space, which cannot be achieved by simple post-processing. Experiments on large-scale human motion datasets show that our approach achieves state-of-the-art motion quality and improves physical plausibility drastically (>78% for all datasets).
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

Limited-Angle Computed Tomography (LACT) is a nondestructive 3D imaging technique used in a variety of applications ranging from security to medicine. The limited angle coverage in LACT is often a dominant source of severe artifacts in the reconstructed images, making it a challenging imaging inverse problem. Diffusion models are a recent class of deep generative models for synthesizing realistic images using image denoisers. In this work, we present DOLCE as the first framework for integrating conditionally-trained diffusion models and explicit physical measurement models for solving imaging inverse problems. DOLCE achieves the SOTA performance in highly ill-posed LACT by alternating between the data-fidelity and sampling updates of a diffusion model conditioned on the transformed sinogram. We show through extensive experimentation that unlike existing methods, DOLCE can synthesize high-quality and structurally coherent 3D volumes by using only 2D conditionally pre-trained diffusion models. We further show on several challenging real LACT datasets that the same pretrained DOLCE model achieves the SOTA performance on drastically different types of images.
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples

The sample selection approach is popular in learning with noisy labels. The state-of-the-art methods train two deep networks simultaneously for sample selection, which aims to employ their different learning abilities. To prevent two networks from converging to a consensus, their divergence should be maintained. Prior work presents that the divergence can be kept by locating the disagreement data on which the prediction labels of the two networks are different. However, this procedure is sample-inefficient for generalization, which means that only a few clean examples can be utilized in training. In this paper, to address the issue, we propose a simple yet effective method called CoDis. In particular, we select possibly clean data that simultaneously have high-discrepancy prediction probabilities between two networks. As selected data have high discrepancies in probabilities, the divergence of two networks can be maintained by training on such data. In addition, the condition of high discrepancies is milder than disagreement, which allows more data to be considered for training, and makes our method more sample-efficient. Moreover, we show that the proposed method enables to mine hard clean examples to help generalization. Empirical results show that CoDis is superior to multiple baselines in the robustness of trained models.
Automated Transformation of PDDL Representations

This paper describes a system that automatically transforms a PDDL encoding, calls a planner to solve the transformed representation, and translates the solution back into the original representation. The approach involves counting objects that are indistinguishable, rather than treating them as individuals, which eliminates some unnecessary combinatorial explosion.
Integration of Augmented Reality into the CAD Process

The enhancement of visualization skills of engineering students is essential for their development of design skills in many fields of engineering.Based on studies of visualization skills of entering first year engineering students, some of them have difficulties in dealing with orthographic applications (projections, orthographic to isometric transformations, etc.).They seem to lack sufficient geometric and/or trigonometric relational skills, both of which are essential when modeling even simple geometric objects.Augmented Reality (AR) technology could provide a solution to this problem.The potential benefits of AR are improvements in the students' abilities with respect to spatial cognition, concept development, decision making as well as design modifications and refinements due to the support for viewing and 'touching' the design.This paper will explore the integration of AR into the Computer Aided Design (CAD) process with SolidWorks software in order to provide an interactive learning experience, in which abstract thinking is supported and concept visualization, exploration and evaluation are facilitated during the various stages of the design process.Such an AR system will permit the students to visualize virtual representations of their 3D CAD models in real-world settings by mixing the 3D CAD model with a webcam view of the students and facilitating real-time interactions between the students and the designed objects.
Leadership Styles and Performance of Public Sector Organizations: The Case of Saudi Arabia

In Saudi Arabia, public organizations have been associated with poor management andperformance. Little is understood about the reasons for such poor performance. Skilledand trained leaders are required to sustain the performance of these organizations.This paper focuses on the effect of leadership styles on the performance of Saudi Arabiapublic organizations. Cross-sectional data of 400 employees working in 16 ministriesunder the Saudi Arabia Government were obtained. The validity and reliability ofthe measurement and structural model were tested. Partial Least Squares StructuralEquation Modeling (PLS-SEM) was applied to test the study hypotheses. The result ofPLS-SEM analysis indicated a significant effect of leadership style on the organizationalperformance at the 0.01 level of significance. Leadership styles play a critical role inimproving organizational performance in Saudi Arabian public organizations. Futureresearch is recommended to explore further the predictors affecting the performanceof public organizations in Saudi Arabia.
Cyclic Isogenies for Abelian Varieties with Real Multiplication

We study quotients of principally polarized abelian varieties with real multiplication by Galois-stable finite subgroups and describe when these quotients are principally polarizable. We use this characterization to provide an algorithm to compute explicit cyclic isogenies from kernel for abelian varieties with real multiplication over finite fields. Our algorithm is polynomial in the size of the finite field as well as in the degree of the isogeny and is based on Mumford's theory of theta functions and theta embeddings. Recently, the algorithm has been successfully applied to obtain new results on the discrete logarithm problem in genus 2 as well as to study the discrete logarithm problem in genus 3.
Everything Is Obvious

The impact of artificial inventors is only starting to be felt, but AI’s rapid improvement means that it may soon outdo people at solving problems in certain areas. This should revolutionize not only research and development but also patent law. The most important requirement to being granted a patent is that an invention must be nonobvious to a hypothetical skilled person who represents an average researcher. As AI increasingly augments average researchers, this should make them more knowledgeable and sophisticated. In turn, this should raise the bar to patentability. Once inventive AI moves from augmenting to automating average researchers, it should directly represent the skilled person in obviousness determinations. As inventive AI continues to improve, this should continue to raise the bar to patentability, eventually rendering innovative activities obvious. To a superintelligent AI, everything will be obvious.
Quantum Computing and Complexity in Art

The author draws on her research experience in quantum computing to discuss the conception and form of an interactive installation, CLOUD. CLOUD explores complexity in the postdigital by referencing the principles of quantum superposition, quantum entanglement and quantum measurement.