Reviewerly/exHarmony huggingface dataset:
Efficient Resume Classification Through Rapid Dataset Creation Using ChatGPT

In this article, is presented a study on the problem of resume classification using machine learning algorithms. The use of machine learning algorithms in natural language processing (NLP) has become increasingly popular as they can accurately classify resumes, saving organizations time and money in the hiring process. The effectiveness of these algorithms relies heavily on the quality and quantity of data used for training neural network models. The choice of the underlying model architecture is crucial for achieving good performance as well as a well-curated dataset is needed for effective training. This is especially true for classification tasks, where accurate annotations are required to achieve high performance. We used the Open AI API [1], in order to generate structured and unstructured resumes based on specific criteria. The generated resumes were cleaned, annotated and used to train a feedforward neural network which relied on Universal Sentence Encoder (USE) [2] embeddings. This approach allowed us to efficiently generate large amounts of annotated data that are tailored to specific occupation categories for our classification task. To evaluate the effectiveness of our model, a dataset of resumes was constructed from indeed.com. This approach ensures that our model is not biased towards working only on the generated resumes but it can also perform very well on real-world data. The model identified the relevant occupation category per resume and achieved an overall accuracy of 98% on the ChatGPT -generated test set and 85% on the indeed.com dataset.
Secret key rate bounds for quantum key distribution with faulty active phase randomization

Abstract Decoy-state quantum key distribution (QKD) is undoubtedly the most efficient solution to handle multi-photon signals emitted by laser sources, and provides the same secret key rate scaling as ideal single-photon sources. It requires, however, that the phase of each emitted pulse is uniformly random. This might be difficult to guarantee in practice, due to inevitable device imperfections and/or the use of an external phase modulator for phase randomization in an active setup, which limits the possible selected phases to a finite set. Here, we investigate the security of decoy-state QKD when the phase is actively randomized by faulty devices, and show that this technique is quite robust to deviations from the ideal uniformly random scenario. For this, we combine a novel parameter estimation technique based on semi-definite programming, with the use of basis mismatched events, to tightly estimate the parameters that determine the achievable secret key rate. In doing so, we demonstrate that our analysis can significantly outperform previous results that address more restricted scenarios.
Extracting structured information from unstructured histopathology reports using generative pre‐trained transformer 4 (<scp>GPT</scp>‐4)

Abstract Deep learning applied to whole‐slide histopathology images (WSIs) has the potential to enhance precision oncology and alleviate the workload of experts. However, developing these models necessitates large amounts of data with ground truth labels, which can be both time‐consuming and expensive to obtain. Pathology reports are typically unstructured or poorly structured texts, and efforts to implement structured reporting templates have been unsuccessful, as these efforts lead to perceived extra workload. In this study, we hypothesised that large language models (LLMs), such as the generative pre‐trained transformer 4 (GPT‐4), can extract structured data from unstructured plain language reports using a zero‐shot approach without requiring any re‐training. We tested this hypothesis by utilising GPT‐4 to extract information from histopathological reports, focusing on two extensive sets of pathology reports for colorectal cancer and glioblastoma. We found a high concordance between LLM‐generated structured data and human‐generated structured data. Consequently, LLMs could potentially be employed routinely to extract ground truth data for machine learning from unstructured pathology reports in the future. © 2023 The Authors. The Journal of Pathology published by John Wiley &amp; Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Simple Threshold (Fully Homomorphic) Encryption from LWE with Polynomial Modulus

The learning with errors (LWE) assumption is a powerful tool for building encryption schemes with useful properties, such as plausible resistance to quantum computers, or support for homomorphic computations. Despite this, essentially the only method of achieving threshold decryption in schemes based on LWE requires a modulus that is superpolynomial in the security parameter, leading to a large overhead in ciphertext sizes and computation time. In this work, we propose a (fully homomorphic) encryption scheme that supports a simple t-out-of-n threshold decryption protocol while allowing for a polynomial modulus. The main idea is to use the Rényi divergence (as opposed to the statistical distance as in previous works) as a measure of distribution closeness. This comes with some technical obstacles, due to the difficulty of using the Rényi divergence in decisional security notions such as standard semantic security. We overcome this by constructing a threshold scheme with a weaker notion of one-way security and then showing how to transform any one-way (fully homomorphic) threshold scheme into one guaranteeing indistinguishability-based security.
A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit

Despite the practical success of deep neural networks, a comprehensive theoretical framework that can predict practically relevant scores, such as the test accuracy, from knowledge of the training data is currently lacking. Huge simplifications arise in the infinite-width limit, in which the number of units Nℓ in each hidden layer (ℓ = 1, …, L, where L is the depth of the network) far exceeds the number P of training examples. This idealization, however, blatantly departs from the reality of deep learning practice. Here we use the toolset of statistical mechanics to overcome these limitations and derive an approximate partition function for fully connected deep neural architectures, which encodes information on the trained models. The computation holds in the thermodynamic limit, where both Nℓ and P are large and their ratio αℓ = P/Nℓ is finite. This advance allows us to obtain: (1) a closed formula for the generalization error associated with a regression task in a one-hidden layer network with finite α1; (2) an approximate expression of the partition function for deep architectures (via an effective action that depends on a finite number of order parameters); and (3) a link between deep neural networks in the proportional asymptotic limit and Student's t-processes. Theoretical frameworks aiming to understand deep learning rely on a so-called infinite-width limit, in which the ratio between the width of hidden layers and the training set size goes to zero. Pacelli and colleagues go beyond this restrictive framework by computing the partition function and generalization properties of fully connected, nonlinear neural networks, both with one and with multiple hidden layers, for the practically more relevant scenario in which the above ratio is finite and arbitrary.
Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion

Insulators find extensive use across diverse facets of power systems, playing a pivotal role in ensuring the security and stability of electrical transmission. Detecting insulators is a fundamental measure to secure the safety and stability of power transmission, with precise insulator positioning being a prerequisite for successful detection. To overcome challenges such as intricate insulator backgrounds, small defect scales, and notable differences in target scales that reduce detection accuracy, we propose the AC-YOLO insulator multi-defect detection network based on adaptive attention fusion. To elaborate, we introduce an adaptive weight distribution multi-head self-attention module designed to concentrate on intricacies in the features, effectively discerning between insulators and various defects. Additionally, an adaptive memory fusion detection head is incorporated to amalgamate multi-scale target features, augmenting the network’s capability to extract insulator defect characteristics. Furthermore, a CBAM attention mechanism is integrated into the backbone network to enhance the detection performance for smaller target defects. Lastly, improvements to the loss function expedite model convergence. This study involved training and evaluation using publicly available datasets for insulator defects. The experimental results reveal that the AC-YOLO model achieves a notable 5.1% enhancement in detection accuracy compared to the baseline. This approach significantly boosts detection precision, diminishes false positive rates, and fulfills real-time insulator localization requirements in power system inspections.
Context Caching for IoT-Based Applications: Opportunities and Challenges

The Internet of Things (IoT) is growing at a rapid pace. Applications using IoT technologies have transformed many activities to be digitized enabling more productivity, economy, and quality of work. Context Management Platforms (CMPs) that unify heterogeneous streams of big IoT data and derive insights of a sensed environment (called context) using inferencing, massively enhance the smartness of IoT-based applications. Handling this massive scale of data and processing in an IoT-ecosystem for context-aware applications are both time and resource consuming, especially consid-ering the bottlenecks in network and processing resources. While traditional data caching is a time-proven technique to enable low-latency delivery of data items for a superior perceived user experience, work in context caching is extremely limited. Context information is different from typically discussed forms of data in many ways. Context-aware traditional data caching techniques have limited applicability to caching context information due to many unique challenges. These challenges can be categorized by features of context, context quality demands, techniques for caching con-text information, and context cache memory technologies. We categorically discuss each challenge in this article supported by real-world scenarios and experimental results. We contend that context caching is distinct from traditional data caching techniques and highlight the importance of con-text caching for time-critical, adaptive, context-aware applications. This article aims to demystify the unique research opportunities and challenges when developing context caching techniques for scale and efficiency objectives of CMPs and provide directions for future work.
Resource Allocation in Cloud Computing Using Genetic Algorithm and Neural Network

Cloud computing is one of the most used distributed systems for data processing and data storage. Due to the continuous increase in the size of the data processed by cloud computing, scheduling multiple tasks to maintain efficiency while reducing idle becomes more and more challenging. Efficient cloud-based scheduling is also highly sought by modern transportation systems to improve their security. In this paper, we propose a hybrid algorithm that leverages genetic algorithms and neural networks to improve scheduling. Our method classifies tasks with the Neural Network Task Classification (N2TC) and sends the selected tasks to the Genetic Algorithm Task Assignment (GATA) to allocate resources. It is fairness aware to prevent starvation and considers the execution time, response time, cost, and system efficiency. Evaluations show that our approach outperforms the state-of-the-art method by 3.2 % at execution time, 13.3 % in costs, and 12.1 % at response time.
Optimization of Boosting Classifier Method for Paddy Growth Phase Classification Using Oversampling and Hyperparameter Tuning

The agricultural sector has a significant contribution to the achievement of the second Sustainable Development Goals (SDGs), which are zero hunger, ensuring food security, enhancing nutrition, and fostering sustainable agriculture. This goal is in line with the regulation issued by the Indonesian Government No. 68 of 2002 regarding food security. To achieve that goal, it is necessary to have timely and accurate agricultural data available. Machine learning can predict paddy growth phases more quickly and efficiently. In this study, the optimization method was carried out on the machine learning Boosting Classifier with the aim of this study being to find out which optimization method has the best performance on the Boosting Classifier model in classifying rice growth phases based on the evaluation results of 7 comparative metrics namely overall accuracy, precision, recall, F1 score, kappa score, cross-validation, and time execution. The optimization Boosting Classifier algorithm is carried out using the hyperopt hyperparameter tuning method, imbalance data handling, and a combination of hyperopt hyperparameter tuning and imbalance data handling. As a result, the hyperopt hyperparameter tuning method is a method that can improve the performance of the Boosting Classifier machine learning model in classifying paddy growth phases based on the results of the values in most of the comparison metrics (6/7 comparison metrics) which are on average higher by 0.04 when compared to the other three methods are the baseline method, the imbalance data handling method, and a combination of hyperopt hyperparameter tuning and imbalance data handling.
Non-Fungible Tokens (NFTs)-Survey of Current Applications, Evolution and Future Directions

Non-fungible tokens (NFTs) have become an exciting technology that provides a fresh perspective on asset ownership, provenance, and value exchange. NFTs, a blockchain-based technology, are distinct and indivisible cryptographic tokens used to confirm and record the ownership of digital and physical assets in an immutable and transparent way. The fundamental block of NFT is a smart contract built on a blockchain network. This contract contains specific information about the asset it represents, such as its unique identifier, metadata, and ownership details. The information is kept private and tamper-proof due to the decentralized and distributed structure of the blockchain, boosting faith in the token’s authenticity. The NFT is gaining popularity, but it is still in the developing stage. There is a need for a comprehensive survey to guide future research and development in NFTs. Thus, this paper presents the technical components of NFTs, their features, and the minting process. Further, this survey paper describes different token standards for NFTs. It presents various applications of NFTs in healthcare, supply chain, gaming, identity verification, agriculture, intellectual property, smart cities, charity and donation, and education. The article also emphasizes the significant difficulties faced currently in implementing NFT technology from the viewpoints of ownership, governance, and property rights, as well as security, privacy, and environmental effects. This work also elucidates the future directions to overcome the challenges in adopting NFTs in various applications.
Shorter quantum circuits via single-qubit gate approximation

We give a novel procedure for approximating general single-qubit unitaries from a finite universal gate set by reducing the problem to a novel magnitude approximation problem, achieving an immediate improvement in sequence length by a factor of 7/9. Extending the works \cite{Hastings2017} and \cite{Campbell2017}, we show that taking probabilistic mixtures of channels to solve fallback \cite{BRS2015} and magnitude approximation problems saves factor of two in approximation costs. In particular, over the Clifford+<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msqrt><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">T</mml:mi></mml:mrow></mml:msqrt></mml:math> gate set we achieve an average non-Clifford gate count of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>0.23</mml:mn><mml:msub><mml:mi>log</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>&amp;#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>&amp;#x03B5;</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo>+</mml:mo><mml:mn>2.13</mml:mn></mml:math> and T-count <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>0.56</mml:mn><mml:msub><mml:mi>log</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>&amp;#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>&amp;#x03B5;</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo>+</mml:mo><mml:mn>5.3</mml:mn></mml:math> with mixed fallback approximations for diamond norm accuracy <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&amp;#x03B5;</mml:mi></mml:math>.This paper provides a holistic overview of gate approximation, in addition to these new insights. We give an end-to-end procedure for gate approximation for general gate sets related to some quaternion algebras, providing pedagogical examples using common fault-tolerant gate sets (V, Clifford+T and Clifford+<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msqrt><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">T</mml:mi></mml:mrow></mml:msqrt></mml:math>). We also provide detailed numerical results for Clifford+T and Clifford+<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msqrt><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">T</mml:mi></mml:mrow></mml:msqrt></mml:math> gate sets. In an effort to keep the paper self-contained, we include an overview of the relevant algorithms for integer point enumeration and relative norm equation solving. We provide a number of further applications of the magnitude approximation problems, as well as improved algorithms for exact synthesis, in the Appendices.
A large-scale dataset of patient summaries for retrieval-based clinical decision support systems

Retrieval-based Clinical Decision Support (ReCDS) can aid clinical workflow by providing relevant literature and similar patients for a given patient. However, the development of ReCDS systems has been severely obstructed by the lack of diverse patient collections and publicly available large-scale patient-level annotation datasets. In this paper, we collect a novel dataset of patient summaries and relations called PMC-Patients to benchmark two ReCDS tasks: Patient-to-Article Retrieval (ReCDS-PAR) and Patient-to-Patient Retrieval (ReCDS-PPR). Specifically, we extract patient summaries from PubMed Central articles using simple heuristics and utilize the PubMed citation graph to define patient-article relevance and patient-patient similarity. PMC-Patients contains 167k patient summaries with 3.1 M patient-article relevance annotations and 293k patient-patient similarity annotations, which is the largest-scale resource for ReCDS and also one of the largest patient collections. Human evaluation and analysis show that PMC-Patients is a diverse dataset with high-quality annotations. We also implement and evaluate several ReCDS systems on the PMC-Patients benchmarks to show its challenges and conduct several case studies to show the clinical utility of PMC-Patients.
Entangled time-crystal phase in an open quantum light-matter system

Time crystals are nonequilibrium many-body phases in which the state of the system dynamically approaches a limit cycle. While these phases have recently been the focus of intensive research, it is still far from clear whether they can host quantum correlations. In fact, mostly classical correlations have been observed so far, and time crystals appear to be effectively classical high-entropy phases. Here, we consider the nonequilibrium behavior of an open quantum light-matter system, realizable in current experiments, which maps to a paradigmatic time-crystal model after an adiabatic elimination of the light field. The system displays a bistable regime with coexistent time-crystal and stationary phases, terminating at a tricritical point from which a second-order phase transition line departs. While light and matter are uncorrelated in the stationary phase, the time-crystal phase features bipartite correlations of both quantum and classical nature. Our work unveils that time-crystal phases in collective open quantum systems can sustain quantum correlations, including entanglement, and are thus more than effectively classical many-body phases.
Storage and Learning Phase Transitions in the Random-Features Hopfield Model

The Hopfield model is a paradigmatic model of neural networks that has been analyzed for many decades in the statistical physics, neuroscience, and machine learning communities. Inspired by the manifold hypothesis in machine learning, we propose and investigate a generalization of the standard setting that we name random-features Hopfield model. Here, P binary patterns of length N are generated by applying to Gaussian vectors sampled in a latent space of dimension D a random projection followed by a nonlinearity. Using the replica method from statistical physics, we derive the phase diagram of the model in the limit P,N,D→∞ with fixed ratios α=P/N and α_{D}=D/N. Besides the usual retrieval phase, where the patterns can be dynamically recovered from some initial corruption, we uncover a new phase where the features characterizing the projection can be recovered instead. We call this phenomena the learning phase transition, as the features are not explicitly given to the model but rather are inferred from the patterns in an unsupervised fashion.
Committing to reproducibility and explainability: using Git as a research journal

Abstract Traditional critical editions in print offer a model for creating a culture of reproducible and explainable research in Digital Humanities. In addition to explaining their editorial methods and practices in a preface, editors provide a critical apparatus to allow readers to evaluate the evidence and decide for themselves whether the editor’s judgment is sound. This paper will argue for viewing distributed, version-controlled data repositories (e.g., Git) in the same way, to empower users to follow the choices and methods of DH researchers and to reproduce their work. Using examples from a current project, this paper will propose some guidelines for using Git to promote reproducibility and explainability in the publication of DH research of all kinds.
Free-electron interactions with photonic GKP states: Universal control and quantum error correction

We show that the coherent interaction between free electrons and photons can be used for universal control of continuous-variable photonic quantum states in the form of Gottesman-Kitaev-Preskill (GKP) qubits. Specifically, we find that electron energy combs enable nondestructive measurements of the photonic state and can induce arbitrary gates. Moreover, a single electron interacting with multiple photonic modes can create highly entangled states such as Greenberger-Horne-Zeilinger states and cluster states of GKPs.