Reviewerly/exHarmony huggingface dataset:
flap: A Deterministic Parser with Fused Lexing

Lexers and parsers are typically defined separately and connected by a token stream. This separate definition is important for modularity and reduces the potential for parsing ambiguity. However, materializing tokens as data structures and case-switching on tokens comes with a cost. We show how to fuse separately-defined lexers and parsers, drastically improving performance without compromising modularity or increasing ambiguity. We propose a deterministic variant of Greibach Normal Form that ensures deterministic parsing with a single token of lookahead and makes fusion strikingly simple, and prove that normalizing context free expressions into the deterministic normal form is semantics-preserving. Our staged parser combinator library, flap, provides a standard interface, but generates specialized token-free code that runs two to six times faster than ocamlyacc on a range of benchmarks.
Partial Label Learning with Gradually Induced Error-Correction Output Codes

Partial label learning (PLL) is a specific weakly supervised learning problem, where each training example is associated with a set of candidate labels while only one of them is the ground truth. Recently, a disambiguation-free partial label learning method based on error-correcting output codes has been proposed, and achieves outstanding performance among existing partial label learning methods. Despite its good performance, it cannot deal with high ambiguity scenario and large candidate label size. To tackle this issue, we propose a new partial label learning method called PL-GECOC that gradually induces error-correction output codes during iterative model training. Experiments show that PL-GECOC outperforms most of the existing methods, especially in high ambiguity and large candidate label size scenarios.
Estimation Model for Enhanced Predictive Object Point Metric in OO Software Size Estimation Using Deep Learning

The Software industry’s rapid growth contributes to the need for new technologies. PRICE software system uses Predictive Object Point (POP) as a size measure to estimate Effort and cost. A refined POP metric value for object-oriented software written in Java can be calculated using the Automated POP Analysis tool. This research used 25 open-source Java projects. The refined POP metric improves the drawbacks of the PRICE system and gives a more accurate size measure of software. This paper uses refined POP metrics with curve-fitting neural networks and multi-layer perceptron neural network-based deep learning to estimate the software development effort. Results show that this approach gives an effort estimate closer to the actual Effort obtained through Constructive Cost Estimation Model (COCOMO) estimation models and thus validates refined POP as a better size measure of object-oriented software than POP. Therefore we consider the MLP approach to help construct the metric for the scale of the Object-Oriented (OO) model system.
D<scp>y</scp>F<scp>ormer</scp> : A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability

Transformers have achieved great success in several domains, including Natural Language Processing and Computer Vision. However, their application to real-world graphs is less explored, mainly due to its high computation cost and its poor generalizability caused by the lack of enough training data in the graph domain. To fill in this gap, we propose a scalable Transformer-like dynamic graph learning method named Dynamic Graph Transformer (DyFormer) with spatial-temporal encoding to effectively learn graph topology and capture implicit links. To achieve efficient and scalable training, we propose temporal-union graph structure and its associated subgraph-based node sampling strategy. To improve the generalization ability, we introduce two complementary self-supervised, pre-training tasks and show that jointly optimizing the two pre-training tasks results in a smaller Bayesian error rate via an information- theoretic analysis. Extensive experiments on the real- world datasets illustrate that DyFormer achieves a consistent 1% ~ 3% AUC gain (averaged over all time steps) compared with baselines on all benchmarks. [Code]
Real-Time IoT-Based Connected Vehicle Infrastructure for Intelligent Transportation Safety

The transportation sector faces severe consequences due to the incrementing population influx yielding congestions, fatalities and haphazard traffic scenarios. Advanced Driver Assistance Systems (ADAS) assists highly in such scenarios by eradicating probable accidents and ensures traffic safety. This paper presents intelligent transportation systems (ITS) approach through the connected vehicle technology infrastructure. YOLO v4 (You Only Look Once) inspired real-time computer vision capable of detecting vehicles, pedestrians and animals at high efficiency (0.9777 mean average precision) is deployed on the GPU (Graphics Processing Unit) which offered higher frame rate of detection (74.26 fps). The locations of animals and potholes were mapped through consistent survey and mobile app which relayed the detected locations to the cloud server forming a geospatial database. Clustered locations from the geospatial database on dense transportation network were utilized for constructing animal and pothole hotspot zones. A basic level of display warning was triggered when the vehicle approached animal and pothole areas. Furthermore, advanced alert comprising of display and sound alert was trigger when the vehicle approached hotspot zones. This was implemented using real-time Internet of things (IoT) and cloud infrastructure applications for continuous vehicle’s location monitoring and triggering as per the hotspot geo-locations. The proposed system ensured traffic safety and assisted in avoiding probable crashes and accidents that generally led to congestions and fatalities.
Automatic Essay Scoring Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses

Deep-learning based Automatic Essay Scoring (AES) systems are being actively used in various high-stake applications in education and testing. However, little research has been put to understand and interpret the black-box nature of deep-learning-based scoring algorithms. While previous studies indicate that scoring models can be easily fooled, in this paper, we explore the reason behind their surprising adversarial brittleness. We utilize recent advances in interpretability to find the extent to which features such as coherence, content, vocabulary, and relevance are important for automated scoring mechanisms. We use this to investigate the oversensitivity (i.e., large change in output score with a little change in input essay content) and overstability (i.e., little change in output scores with large changes in input essay content) of AES. Our results indicate that autoscoring models, despite getting trained as “end-to-end” models with rich contextual embeddings such as BERT, behave like bag-of-words models. A few words determine the essay score without the requirement of any context making the model largely overstable. This is in stark contrast to recent probing studies on pre-trained representation learning models, which show that rich linguistic features such as parts-of-speech and morphology are encoded by them. Further, we also find that the models have learnt dataset biases, making them oversensitive. The presence of a few words with high co-occurrence with a certain score class makes the model associate the essay sample with that score. This causes score changes in ∼95% of samples with an addition of only a few words. To deal with these issues, we propose detection-based protection models that can detect oversensitivity and samples causing overstability with high accuracies. We find that our proposed models are able to detect unusual attribution patterns and flag adversarial samples successfully.
Decoupling Graph Neural Network with Contrastive Learning for Fraud Detection

Recently, many fraud detection models introduced graph neural networks (GNNs) to improve the model performance. However, fraudsters often disguise themselves by camouflaging their features or relations. Due to the aggregation nature of GNNs, information from both input features and graph structure will be compressed for representation learning simultaneously. On the one hand, since not all neighbors provide useful information due to camouflage, aggregating information from all neighbors may potentially decrease the model performance. On the other hand, the structure including all neighbors is not reliable due to the relation camouflage. In this paper, we propose to decouple attribute learning and structure learning to avoid the mutual influence of feature and relation camouflage. Therefore, the model first learns its embedding seperately and then combine them together with label-guided contrastive losses to make predictions better. We conduct extensive experiments on two real-world datasets, and the results show the effectiveness of the proposed model.
Efficient Execution of Blockchain Transactions Through Deterministic Concurrency Control

Concurrently executing Blockchain transactions can make good use of modern hardware and improve system performance. Recent works mainly utilize a dependency graph to represent the partial order among conflicting transactions. All participants can then execute the transactions concurrently according to an identical order and keep consistent with each other. However generating dependency graphs is quite time-consuming, and during this process some transactions have to be re-executed multiple times. In this paper, we adopt deterministic concurrency control to quickly compute the partial order. Instead of one large graph, we partition the transactions into a sequence of batches. In this way, there is no need to deal with the inter-batch conflicts in finding the partial order. We also propose a two stage approach (DVC) to find a partial order with high degree of parallelism. DVC does not need to re-execute transactions, and can find an approximate optimal partial order by solving an equivalent MinVWC problem. We integrate the proposed techniques into an open-source system and compare them with several advanced solutions. As shown by the experimental results, our approaches can significantly reduce the costs of computing the partial order and obtain a schedule of high parallelism degree.
Feature Ranking Merging: FRmgg. Application in High Dimensionality Binary Classification Problems

This paper introduces a framework for feature selection. Firstly, it establishes a percentage in order to set a cut-off point in the context of feature ranking based on feature selection. Secondly, it combines a couple of feature ranking methods using a common percentage from those features. This combination has a weak to a strong influence in the class label that is run in an independent way and merges the solutions achieved by every ranking-based feature selection. The proposed methodology has been called Feature Ranking merging (FRmgg). It has been tested with three high-dimensionality binary classification data sets, which have been assessed with three classifiers and two measures have been reported. The results are acceptable since the feature space reduction is convenient compared to the raw data set and the results in some cases are better.
Cyclical Curriculum Learning

Artificial neural networks (ANNs) are inspired by human learning. However, unlike human education, classical ANN does not use a curriculum. Curriculum learning (CL) refers to the process of ANN training in which samples are used in a meaningful order. When using CL, training begins with a subset of the dataset and new samples are added throughout the training, or training begins with the entire dataset and the number of samples used is reduced. With these changes in training dataset size, better results can be obtained with curriculum, anti-curriculum, or random-curriculum methods than the vanilla method. However, a generally efficient CL method for various architectures and datasets is not found. In this article, we propose cyclical CL (CCL), in which the data size used during training changes cyclically rather than simply increasing or decreasing. Instead of using only the vanilla method or only the curriculum method, using both methods cyclically like in CCL provides more successful results. We tested the method on 18 different datasets and 15 architectures in image and text classification tasks and obtained more successful results than no-CL and existing CL methods. We also have shown theoretically that it is less erroneous to apply CL and vanilla cyclically instead of using only CL or only the vanilla method. The code of the cyclical curriculum is available at https://github.com/CyclicalCurriculum/Cyclical-Curriculum.
METAVERSE-BASED ONLINE ENGLISH TEACHING SCHEME IN MULTI-SOURCE AND CROSS-DOMAIN ENVIRONMENT

Metaverse empowers learning and spawns learning metaverse. Learning Metaverse is a new type of Internet educational application and digital social ecosystem. Learning Metaverse Empowering Education will help promote the construction of new educational infrastructure and provide new opportunities for building a high-quality education support system. This paper aims at the online English teaching scenario in the multi-source and cross-domain environment, and constructs a new teaching scheme by using the metaverse technology. In order to improve the quality of English teaching and the interest of English teaching classroom, this paper proposes an immersive English learning visualization system with diversified interactive methods. Therefore, this paper designs an immersive interaction management method based on visual scene and a visual network management method based on visual algorithm tree, which can systematically manage user interactions and provide full-process support for visual operations in an immersive environment. In order to verify the effectiveness and naturalness of the interactive system, this paper conducts a comparative experiment between the two-dimensional desktop system and the immersive virtual reality system. The user experiment results show that the system can meet the needs of multi-source and cross-domain environment. The new online English teaching scheme proposed in this paper can provide users with efficient and natural interaction in the immersive visual English learning scenario.
Cloud based Single Shot Detector Model for Speed Breaker Detection

Speed breaker-related accidents are on the rise. Irregular use of speed breakers at odd positions contributes to accidents. To tackle this problem a cloud-based speed breaker detection system has been developed. It is a deep learning-based approach. Single Shot Detector (SSD) for MobileNetV2 architecture is used for detection. Detection metrics based on the Common Objects in Context (COCO) dataset were utilized for performance evaluation. The model achieved a mean average precision of 97.19 % at 50% intersection of union. This showcases the ability of the model to detect speed breakers on the road correctly. The model is hosted on the Microsoft Azure cloud platform which processes images from the ESP32 Wi-Fi Cam Module. An application that continuously interacts with the cloud-based deep learning model is also developed. It displays an alert if the cloud-based model detects a speed breaker
Studytube: Educational Bloggers in the Higher Education Ecosystem

The article examines a new non–traditional actor in higher education: educational bloggers “studytubers” who being students themselves record informal and non-professional videos on the topic of learning, productivity, popular science, and education in general. The objectives of this study included a description of the phenomenon of the studytube itself, its history, context, specifics, then in revealing the ways educational bloggers work and, finally, in fixing its role in the ecosystem of higher education. The phenomenon of studytube was considered through an ecosystem approach, the framework of the “participatory culture”, and the concept of “micro-celebrity”. It was empirically studied through the methodology of digital ethnography and methods of nonparticipant observation, visual analysis of video materials, thematic analysis of texts. The results showed the main thematic repertoire of the educational bloggers, the stylistic features of their work, the affective nature of the work of educational bloggers, ways of interacting with subscribers, and monetization models. Recommendations are also offered to universities for the involvement of the studytube community into the development of the university environment.
Safe Navigation and Obstacle Avoidance Using Differentiable Optimization Based Control Barrier Functions

Control barrier functions (CBFs) have been widely applied to safety-critical robotic applications. However, the construction of control barrier functions for robotic systems remains a challenging task. Recently, collision detection using differentiable optimization has provided a way to compute the minimum uniform scaling factor that results in an intersection between two convex shapes and to also compute the Jacobian of the scaling factor. In this letter, we propose a framework that uses this scaling factor, with an offset, to systematically define a CBF for obstacle avoidance tasks. We provide theoretical analyses of the continuity and continuous differentiability of the proposed CBF. We empirically evaluate the proposed CBF's behavior and show that the resulting optimal control problem is computationally efficient, which makes it applicable for real-time robotic control. We validate our approach, first using a 2D mobile robot example, then on the Franka-Emika Research 3 (FR3) robot manipulator both in simulation and experiment.
Detection of Real Time Anomalous Activities using YOLOv7

India has seen an increase in crime rates over the past several years. The National Crime Records Bureau reports that the number of instances has significantly dimbed over the last few years, rising from 383.5 in 2018 to 385.5 in 2019 to 383.8 in 2020. In the present, more crimes are being reported, including accidents, reckless driving, chain stealing, etc. Governments and police departments are taking a variety of actions to reduce these crimes, including enforcing fines, launching awareness campaigns, etc. They are also utilizing modern technologies, such as video analytics through CCTV camera surveillance. To this day, these technologies are solely used to identify reverse driving, no paths, loitering, crowd gathering, and zone monitoring. In order to lessen the aforementioned behaviors, we'll implement a video analytic technique in remaining domains like accidents, rash driving, chain-snatching, etc. ML framework of You Only Look Once version 7 (YOLOv7) for video processing and a control room alarm notification system will be used to develop this video analytic technique. The proposed work intends to establish a secure society in order to benefit the younger, more promising generations.
Optimal configuration of a superconducting photon number resolving detector

A photon number resolving detector (PNRD) is a device providing a different output depending on the number of incident photons in the single or few photons regime. This tool is crucial in several applications such as quantum communication, boson sampling, photon sources characterization and so on. PNRD are not new players in ultraviolet or visible wavelengths, but superconducting nanostrips provide a performing counterpart also in the near-infrared, where the aforementioned applications would like to operate. In this work we present a comprehensive description of the operation of an eight-pixel PNRD at the telecom wavelength of 1550 nm.
Clinical encounter heterogeneity and methods for resolving in networked EHR data: a study from N3C and RECOVER programs

Abstract Objective Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite “macrovisits.” Materials and Methods Encounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits. Results Atomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay (LOS) and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, LOS and measurement variance decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability. Discussion Encounters are a complex and heterogeneous component of EHR data and native data issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations, and developments are necessary to realize the full potential of modern real-world data. Conclusion This article presents method developments to manipulate and resolve EHR encounter data issues in a generalizable way as a foundation for future research and analysis.
BlockArch’23 - Fourth International Workshop on Blockchain-Based Architecture

Welcome to the Fourth Edition of the International Workshop on Blockchain-Based Architecture. Blockchain technology’s transformative capabilities have only continued to grow in recent years, as it is increasingly recognized as a turning point in many use case scenarios beyond the financial sector. From supply chain management to voting systems to digital identity, the potential applications of blockchain technology are vast and varied.