Reviewerly/exHarmony huggingface dataset:
An Accurate Identifier Renaming Prediction and Suggestion Approach

Identifiers play an important role in helping developers analyze and comprehend source code. However, many identifiers exist that are inconsistent with the corresponding code conventions or semantic functions, leading to flawed identifiers. Hence, identifiers need to be renamed regularly. Even though researchers have proposed several approaches to identify identifiers that need renaming and further suggest correct identifiers for them, these approaches only focus on a single or a limited number of granularities of identifiers without universally considering all the granularities and suggest a series of sub-tokens for composing identifiers without completely generating new identifiers. In this article, we propose a novel identifier renaming prediction and suggestion approach. Specifically, given a set of training source code, we first extract all the identifiers in multiple granularities. Then, we design and extract five groups of features from identifiers to capture inherent properties of identifiers themselves and the relationships between identifiers and code conventions, as well as other related code entities, enclosing files, and change history. By parsing the change history of identifiers, we can figure out whether specific identifiers have been renamed or not. These identifier features and their renaming history are used to train a Random Forest classifier, which can be further used to predict whether a given new identifier needs to be renamed or not. Subsequently, for the identifiers that need renaming, we extract all the related code entities and their renaming change history. Based on the intuition that identifiers are co-evolved as their relevant code entities with similar patterns and renaming sequences, we could suggest and recommend a series of new identifiers for those identifiers. We conduct extensive experiments to validate our approach in both the Java projects and the Android projects. Experimental results demonstrate that our approach could identify identifiers that need renaming with an average F-measure of more than 89%, which outperforms the state-of-the-art approach by 8.30% in the Java projects and 21.38% in the Android projects. In addition, our approach achieves a Hit@10 of 48.58% and 40.97% in the Java and Android projects in suggesting correct identifiers and outperforms the state-of-the-art approach by 29.62% and 15.75%, respectively.
TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning

Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and remote applications. However, deploying interpretable artificial intelligence systems that can perform high-level symbolic reasoning and satisfy the underlying system rules and physics within the tight platform resource constraints is challenging. In this paper, we introduce TinyNS , the first platform-aware neurosymbolic architecture search framework for joint optimization of symbolic and neural operators. TinyNS provides recipes and parsers to automatically write microcontroller code for five types of neurosymbolic models, combining the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models. TinyNS uses a fast, gradient-free, black-box Bayesian optimizer over discontinuous, conditional, numeric, and categorical search spaces to find the best synergy of symbolic code and neural networks within the hardware resource budget. To guarantee deployability, TinyNS talks to the target hardware during the optimization process. We showcase the utility of TinyNS by deploying microcontroller-class neurosymbolic models through several case studies. In all use cases, TinyNS outperforms purely neural or purely symbolic approaches while guaranteeing execution on real hardware.
Single-Shuffle Card-Based Protocol with Eight Cards per Gate

Card-based cryptography allows us to securely compute arbitrary functions using a deck of physical cards. Its performance is mainly measured by the number of used cards and shuffles, and there is a line of work that aims to reduce either of them. One of the seminal work is by Shinagawa and Nuida (Discrete Applied Mathematics 2021) that shows any Boolean function can be constructed by shuffling only once based on the garbling scheme. Their construction requires $$2n + 24g$$ cards for an n-input Boolean function that is represented by g logical gates. In this paper, we reduce the number of cards to $$2n + 8g$$ for arbitrary functions while keeping it working with only one shuffle.
Chemically aware unitary coupled cluster with <i>ab initio</i> calculations on an ion trap quantum computer: A refrigerant chemicals’ application

Circuit depth reduction is of critical importance for quantum chemistry simulations on current and near term quantum computers. This issue is tackled by introducing a chemically aware strategy for the unitary coupled cluster ansatz. The objective is to use the chemical description of a system to aid in the synthesis of a quantum circuit. We combine this approach with two flavors of symmetry verification for the reduction of experimental noise. These methods enable the use of Quantinuum's System Model H1 ion trap quantum computer for a 6-qubit quantum subspace expansion calculation. We present (i) calculations to obtain methane's optical spectra; (ii) an atmospheric gas reaction simulation involving [CH3⋅-H-OH]‡. Using our chemically aware unitary coupled cluster state-preparation strategy in tandem with state of the art symmetry verification methods, we improve device yield for CH4 at 6 qubits. This is demonstrated by a 90% improvement in two-qubit gate count and a reduction in relative error to 0.2% for electronic energy calculated on System Model H1.
Proving knowledge of isogenies: a survey

Isogeny-based cryptography is an active area of research in post-quantum public key cryptography. The problem of proving knowledge of an isogeny is a natural problem that has several applications in isogeny-based cryptography, such as allowing users to demonstrate that they are behaving honestly in a protocol. It is also related to isogeny-based digital signatures. Over the last few years, there have been a number of advances in this area, but there are still many open problems. This paper aims to give an overview of the topic and highlight some open problems and directions for future research.
Evaluating Language Models for Knowledge Base Completion

Structured knowledge bases (KBs) are a foundation of many intelligent applications, yet are notoriously incomplete. Language models (LMs) have recently been proposed for unsupervised knowledge base completion (KBC), yet, despite encouraging initial results, questions regarding their suitability remain open. Existing evaluations often fall short because they only evaluate on popular subjects, or sample already existing facts from KBs. In this work, we introduce a novel, more challenging benchmark dataset, and a methodology tailored for a realistic assessment of the KBC potential of LMs. For automated assessment, we curate a dataset called WD-Known, which provides an unbiased random sample of Wikidata, containing over 3.9 million facts. In a second step, we perform a human evaluation on predictions that are not yet in the KB, as only this provides real insights into the added value over existing KBs. Our key finding is that biases in dataset conception of previous benchmarks lead to a systematic overestimate of LM performance for KBC. However, our results also reveal strong areas of LMs. We could, for example, perform a significant completion of Wikidata on the relations nativeLanguage, by a factor of $$\sim $$ 21 (from 260k to 5.8M) at $$82\%$$ precision, and citizenOf by a factor of $$\sim $$ 0.3 (from 4.2M to 5.3M) at 90% precision. Moreover, we find that LMs possess surprisingly strong generalization capabilities: even on relations where most facts were not directly observed in LM training, prediction quality can be high. We open-source the benchmark dataset and code. ( https://github.com/bveseli/LMsForKBC ).
Scholarly recommendation systems: a literature survey

Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain.
Spatio-Temporal Features based Human Action Recognition using Convolutional Long Short-Term Deep Neural Network

Recognition of human intention is crucial and challenging due to subtle motion patterns of a series of action evolutions.Understanding of human actions is the foundation of many applications, i.e., human robot interaction, smart video monitoring and autonomous driving etc. Existing deep learning methods use either spatial or temporal features during training.This research focuses on developing a lightweight method using both spatial and temporal features to predict human intention correctly.This research proposes Convolutional Long Short-Term Deep Network (CLSTDN) consists of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).CNN uses Inception-ResNet-v2 to classify object specific class categories by extracting spatial features and RNN uses Long Short-Term Memory (LSTM) for final prediction based on temporal features.Proposed method was validated on four challenging benchmark dataset, i.e., UCF Sports, UCF-11, KTH and UCF-50.Performance of the proposed method was evaluated using seven performance metrics, i.e., accuracy, precision, recall, f-measure, error rate, loss and confusion matrix.Proposed method showed better results comparing with existing research results.Proposed method is expected to encourage researchers to use in future for real time implications to predict human intentions more robustly.
Indian sign language recognition using convolution neural network

The goal of the project is to create a machine learning model that can classify the numerous hand motions used in sign language fingerspelling. Communication with deaf and dumb persons is frequently difficult. A variety of hand, finger, and arm motions that assist the deaf and hard of hearing in communicating with others and vice versa. Classification machine learning algorithms are taught on a set of image data in this userindependent model, and testing is done on a completely other set of data. For some people with particular needs, sign language is their only means of communicating their thoughts and feelings. It enables individuals to understand the world around them by visual descriptions and hence contribute to society. As a result, our model aids us in solving the problem more broadly. By watching the user’s hand gestures, this transforms sign language to regular words.
Temporal dependence Mahalanobis distance for anomaly detection in multivariate spacecraft telemetry series

Spacecraft telemetry data are real-time data as the only basis for ground operation station and management system to judge the working performance and health status of spacecrafts in orbit. Telemetry data are high dimension, strong-dependent, and pseudo-periodic series, which bring great challenges to traditional anomaly detection methods of multivariate parameters. In this case, with the advantages of strong feature extraction and space injection ability, Mahalanobis distance (MD)-based approach has been a strong foundation for industrial system health monitoring. However, the typical MD-based method performs anomaly detection with a fixed threshold for MD series without capturing temporal evolution which cause high false alarms or missing alarms for complex abnormal modes. In this work, the temporal dependence Mahalanobis distance (TDMD) is realized based on multi-factors prediction which can effectively detect contextual and collective anomalies in multivariate telemetry series. Upper and lower limits with time series correlation and dynamic characteristics for the MD of each arriving multivariate point are constructed for online testing. Adequate experiments on simulated and real telemetry series verify the effectiveness and applicability of the proposed method.
When Long Jumps Fall Short: Control-Flow Tracking and Misuse Detection for Non-local Jumps in C

The C programming language offers setjmp/longjmp as a mechanism for non-local control flow. This mechanism has complicated semantics. As most developers do not encounter it day-to-day, they may be unfamiliar with all its intricacies – leading to subtle programming errors. At the same time, most static analyzers lack proper support, implying that otherwise sound tools miss whole classes of program deficiencies. We propose an approach for lifting existing interprocedural analyses to support setjmp/longjmp, as well as to flag their misuse. To deal with the non-local semantics, our approach leverages side-effecting transfer functions which, when executed, may trigger contributions to extra program points. We showcase our analysis on real-world examples and propose a set of litmus tests for other analyzers.
Iris-Wasm: Robust and Modular Verification of WebAssembly Programs

WebAssembly makes it possible to run C/C++ applications on the web with near-native performance. A WebAssembly program is expressed as a collection of higher-order ML-like modules, which are composed together through a system of explicit imports and exports using a host language, enabling a form of higher- order modular programming. We present Iris-Wasm, a mechanized higher-order separation logic building on a specification of Wasm 1.0 mechanized in Coq and the Iris framework. Using Iris-Wasm, we are able to specify and verify individual modules separately, and then compose them modularly in a simple host language featuring the core operations of the WebAssembly JavaScript Interface. Building on Iris-Wasm, we develop a logical relation that enforces robust safety: unknown, adversarial code can only affect other modules through the functions that they explicitly export. Together, the program logic and the logical relation allow us to formally verify functional correctness of WebAssembly programs, even when they invoke and are invoked by unknown code, thereby demonstrating that WebAssembly enforces strong isolation between modules.
Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis

Breast Cancer (BC) is a major health issue in women of the age group above 45. Identification of BC at an earlier stage is important to reduce the mortality rate. Image-based noninvasive methods are used for early detection and for providing appropriate treatment. Computer-Aided Diagnosis (CAD) schemes can support radiologists in making correct decisions. Computational intelligence paradigms such as Machine Learning (ML) and Deep Learning (DL) have been used in the recent past in CAD systems to accelerate diagnosis. ML techniques are feature driven and require a high amount of domain expertise. However, DL approaches make decisions directly from the image. The current advancement in DL approaches for early diagnosis of BC is the motivation behind this review. This article throws light on various types of CAD approaches used in BC detection and diagnosis. A survey on DL, Transfer Learning, and DL-based CAD approaches for the diagnosis of BC is presented in detail. A comparative study on techniques, datasets, and performance metrics used in state-of-the-art literature in BC diagnosis is also summarized. The proposed work provides a review of recent advancements in DL techniques for enhancing BC diagnosis.
Breast Cancer Classification Using Outlier Detection and Variance Inflation Factor

In terms of malignant tumors, breast cancer is one of the most prevalent. Breast cancer is a form of cancer that develops in the breast tissue when the surrounding, healthy breast tissue is overtaken by the uncontrollably growing cells in the breast tissue. Several features or patient conditions can be used in a machine learning approach to predict breast cancer. Machine learning will be utilized in these situations to determine if the cancer is malignant or benign. The Wisconsin Breast Cancer (Diagnostic) Data Set, which contains 32 characteristics and 569 collected data, was the dataset used in this research.. Feature selection in this study is done by eliminating outliers using the upper and lower quartile of each feature then feature selection is also carried out on features that have features that have a high variance inflation factor. The machine learning methods used in this research are Logistic Regression, Random Forest, KNN, SVC, XG Boost, Gradient Boosting, and Ridge Classifier. The selection of this method is based on the target that will be predicted by 2 labels, namely benign cancer, and malignant cancer. The result obtained is that the selection of features using the variance inflation factor increases the accuracy of the previous Logistic Regression and Random Forest methods from 98.25% to 99.12%. The method that has the highest level of accuracy is the Logistic Regression and Random Forest methods which have a value of 99.12%. The next research will be developed by trying other optimization techniques for hyperparameter tuning.
FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning

Unidentified devices in a network can result in devastating consequences. It is, therefore, necessary to fingerprint and identify IoT devices connected to private or critical networks. With the proliferation of massive but heterogeneous IoT devices, it is getting challenging to detect vulnerable devices connected to networks. Current machine learning-based techniques for fingerprinting and identifying devices necessitate a significant amount of data gathered from IoT networks that must be transmitted to a central cloud. Nevertheless, private IoT data cannot be shared with the central cloud in numerous sensitive scenarios. Federated learning (FL) has been regarded as a promising paradigm for decentralized learning and has been applied in many different use cases. It enables machine learning models to be trained in a privacy-preserving way. In this article, we propose a privacy-preserved IoT device fingerprinting and identification mechanisms using FL; we call it FL4IoT. FL4IoT is a two-phased system combining unsupervised-learning-based device fingerprinting and supervised-learning-based device identification. FL4IoT shows its practicality in different performance metrics in a federated and centralized setup. For instance, in the best cases, empirical results show that FL4IoT achieves ∼99% accuracy and F1-Score in identifying IoT devices using a federated setup without exposing any private data to a centralized cloud entity. In addition, FL4IoT can detect spoofed devices with over 99% accuracy .
CADM: Confusion Model-Based Detection Method for Real-Drift in Chunk Data Stream

Concept drift detection has attracted considerable attention due to its importance in many real-world applications such as health monitoring and fault diagnosis. Conventionally, most advanced approaches will be of poor performance when the evaluation criteria of the environment has changed (i.e. concept drift), either can only detect and adapt to virtual drift. In this paper, we propose a new approach to detect real-drift in the chunk data stream with limited annotations based on concept confusion. When a new data chunk arrives, we use both real labels and pseudo labels to update the model after prediction and drift detection. In this context, the model will be confused and yields prediction difference once drift occurs. We then adopt cosine similarity to measure the difference. And an adaptive threshold method is proposed to find the abnormal value. Experiments show that our method has a low false alarm rate and false negative rate with the utilization of different classifiers.
The Evolution of Bitcoin: A Historical Analysis and Future Prospects

Bitcoin is the first decentralized digital currency that was introduced in 2009 and has since garnered significant attention from investors, governments, and the general public. This research article provides a comprehensive historical analysis of the evolution of Bitcoin, tracing its roots from its early days as a niche, experimental technology to its current status as a global financial phenomenon. The study explores the key events and milestones in Bitcoin's development, including the creation of the first block chain, the emergence of cryptocurrency exchanges, and the proliferation of Bitcoin mining. It also delves into the economic and social factors that have influenced Bitcoin's growth and adoption over time, including government regulation, market fluctuations, and the rise of alternative cryptocurrencies. Furthermore, the article examines the potential future prospects of Bitcoin, including its potential to disrupt traditional financial systems, its viability as a long-term investment asset, and the emerging trends in the broader cryptocurrency ecosystem. The study concludes by highlighting the significance of Bitcoin as a transformative technology with significant implications for the global financial system. The insights provided by this research can aid policymakers, investors, and researchers in understanding the past, present, and future prospects of Bitcoin and its role in shaping the future of finance.