Reviewerly/exHarmony huggingface dataset:
A reinforcement learning application of a guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy

Current beam orientation optimization algorithms for radiotherapy, such as column generation (CG), are typically heuristic or greedy in nature because of the size of the combinatorial problem, which leads to suboptimal solutions. We propose a reinforcement learning strategy using Monte Carlo Tree Search that can find a better beam orientation set in less time than CG. We utilize a reinforcement learning structure involving a supervised learning network to guide the Monte Carlo Tree Search and to explore the decision space of beam orientation selection problems. We previously trained a deep neural network (DNN) that takes in the patient anatomy, organ weights, and current beams, then approximates beam fitness values to indicate the next best beam to add. Here, we use this DNN to probabilistically guide the traversal of the branches of the Monte Carlo decision tree to add a new beam to the plan. To assess the feasibility of the algorithm, we used a test set of 13 prostate cancer patients, distinct from the 57 patients originally used to train and validate the DNN, to solve for 5-beam plans. To show the strength of the guided Monte Carlo tree search (GTS) compared to other search methods, we also provided the performances of guided search, uniform tree search and random search algorithms. On average, GTS outperformed all other methods. It found a better solution than CG in 237 seconds on average, compared to 360 seconds for CG, and outperformed all other methods in finding a solution with a lower objective function value in less than 1000 seconds. Using our guided tree search (GTS) method, we could maintain planning target volume (PTV) coverage within 1% error similar to CG, while reducing the organ-at-risk (OAR) mean dose for body, rectum, left and right femoral heads; mean dose to bladder was 1% higher with GTS than with CG.
On the Relationship Between Technical Debt Management and Process Models

This study investigates the potential relationship between development process models and technical debt effects and their management by surveying 432 practitioners from software organizations in Brazil, Chile, Colombia, and the United States.
Slope Stability Analysis Based on Leader Dolphins Herd Algorithm and Simplified Bishop Method

According to the principles of bionics and predatory behavior of dolphins, Leader Dolphin Herd Algorithm (LDHA) was designed, whose performance is also tested on the basis of heuristic algorithm, showing that LDHA is obviously superior to the other algorithms in calculation accuracy and efficiency. The minimum safety factor of sliding surface was taken as the objective function, the recurrence formula of safety factor based on simplified Bishop Method was regarded as constraint condition, a non-linear multi-objective optimization mathematical model used for analyzing slope stability was established. the process of searching slope sliding surface and solving safety factor were designed on account of LDHA. By taking homogeneous slope and layered slope as calculation examples compared to other intelligent algorithms, the results show that the calculation result of LDHA is close to that of Genetic Algorithm and Simulated Annealing Algorithm and improved Genetic Algorithm. Moreover, the LDHA has less iterations and faster convergence rate, and the result is closer to the optimal solution, which indicates the feasibility and practicability of LDHA in solving the minimum stability safety factor.
Interpretability in neural networks towards universal consistency

In the challenge of Artificial Intelligence in processing semantically evaluable information, the application of deep learning techniques depends not only on the algorithms, but also on the principles that explain how they work. The malfunction of a machine learning system, ML, can occur due lack of knowledge of the algorithm intended behavior. The difficulty in debugging ML can be overcome by using strategies based on the universal structure of language that overlaps in the cognitive architecture of biological and intelligent systems. The appropriate choice of an algorithm inspired by the functioning of human language offers the computational scientist methodological strategies to clarify its performance analysis to optimize the interpretative activity under the good instrumentation of the system and to reach the performance level of an application considered safe. Neurolinguistic principles that link interpretation to language and cognition; the semantic dimension that arises not only from the linguistic system, but also from the context in which the information is produced; and the theoretical bases for understanding language as a 'form' (process) and not as a substance (set of signs) provide the groundwork for the intelligent systems’ improvement so that they have universal consistency and lessen the effects of the ‘curse of dimensionality’ or of the bias in the interpretation by the system. Semantics and statistics are considered to understand universal consistency as opposed to ideal consistency when evaluating a data set, since training alone is not sufficient to avoid data manipulation. We conclude that the 'key' for a good information classifier to achieve an acceptable performance of neural networks is in the dynamic aspect of language (language as a form / process) that: Guides the apprehension of how neural networks have access to weights (values); replicates this for intelligent systems making them invariant to many input transformations and guarantees an infinite amount of finite sample information, avoiding semantic distortion.
Utility-Optimized Synthesis of Differentially Private Location Traces

Differentially private location trace synthesis (DPLTS) has recently emerged as a solution to protect mobile users' privacy while enabling the analysis and sharing of their location traces. A key challenge in DPLTS is to best preserve the utility in location trace datasets, which is non-trivial considering the high dimensionality, complexity and heterogeneity of datasets, as well as the diverse types and notions of utility. In this paper, we present OptaTrace: a utility-optimized and targeted approach to DPLTS. Given a real trace dataset D, the differential privacy parameter ε controlling the strength of privacy protection, and the utility/error metric Err of interest; OptaTrace uses Bayesian optimization to optimize DPLTS such that the output error (measured in terms of given metric Err) is minimized while ε-differential privacy is satisfied. In addition, OptaTrace introduces a utility module that contains several built-in error metrics for utility benchmarking and for choosing Err, as well as a front-end web interface for accessible and interactive DPLTS service. Experiments show that OptaTrace's optimized output can yield substantial utility improvement and error reduction compared to previous work.
Text Detection Using Multi-Stage Region Proposal Network Sensitive to Text Scale

Recently, attention has surged concerning intelligent sensors using text detection. However, there are challenges in detecting small texts. To solve this problem, we propose a novel text detection CNN (convolutional neural network) architecture sensitive to text scale. We extract multi-resolution feature maps in multi-stage convolution layers that have been employed to prevent losing information and maintain the feature size. In addition, we developed the CNN considering the receptive field size to generate proposal stages. The experimental results show the importance of the receptive field size.
Refactoring of Code to Remove Technical Debt and Reduce Maintenance Effort

Software development organizations face issue like Technical Debt in their software projects. Technical Debt (TD) is incurred when a person involved in engineering of software, intentionally or unintentionally makes wrong or non-optimal design decisions. This problem occurs due to non-systematic and undefined approach to manage the high level of uncertainty in requirements. These non-optimal design decisions and the nonsystematic approach can result in introduction of code smells. Code smells are actually technical debt (also known as perceived debt), which may cause a project to fade out with time. Code of a software project with technical debt is considered as unclean code. Refactoring is an activity of modifying the code to remove technical debt from a software project and make the project code clean. Goal of this work is to study the impact of removing TD on the effort required to add features and remove bugs from software. To this end, this work uses a stepwise approach; first code smells (considered as TD) are identified, and in the next step the smells are removed and impact of removing the code smells (on removing bug and adding new feature) is observed in terms of effort required during maintenance. Technical debt in five open source software applications has been calculated and impact of removing the code smells has been observed on one of the applications named NopCommerce (an ecommerce-based system). Effort to add a new feature and remove a bug, before and after refactoring, has been calculated in terms of man-hours. The effort required to add new feature and remove a bug from the clean code has been 7% less as compared to the effort required to do the same in unclean code. Whether to perform refactoring or not is a decision made by software developers. The impact of refactoring in reducing effort required to maintain code will help a developer make a decision regarding refactoring.
Remote sensing retrieval of saline and alkaline land based on reflectance spectroscopy and RV-MELM in Zhenlai County

The management of saline and alkaline land requires accurate distribution data, so it is necessary to construct a high-performance inversion model to get distribution data. The paper gets data of reflectance spectroscopy and salt content based on 62 samples in Zhenlai County. Then the paper builds the inversion model and calibration model, and draws a distribution map of salt content to make governance recommendations. The paper proposes the two-hidden-layer extreme learning machine with random values (RV-TELM) and the multiple hidden layers extreme learning machine with random values (RV-MELM) by using random value. Based on whether to specify the root mean square error (RMSE) and coefficient of determination (R2) of the test set that you want to obtain in the end, RV-MELM can be divided into two categories: RV-MELM with specified root mean square error and coefficient of determination (SRV-MELM) and RV-MELM with the largest number of searches (LRV-MELM). The paper selects LRV-MELM as the inversion model, and SRV-MELM as the calibration model. The experimental results show that the overall accuracy of the final map is 1.5286.
Quality Management of Machine Learning Systems

In the past decade, Artificial Intelligence (AI) has become a part of our daily lives due to major advances in Machine Learning (ML) techniques. In spite of an explosive growth in the raw AI technology and in consumer facing applications on the internet, its adoption in business applications has conspicuously lagged behind. For business/mission-critical systems, serious concerns about reliability and maintainability of AI applications remain. Due to the statistical nature of the output, software ‘defects’ are not well defined. Consequently, many traditional quality management techniques such as program debugging, static code analysis, functional testing, etc. have to be reevaluated. Beyond the correctness of an AI model, many other new quality attributes, such as fairness, robustness, explainability, transparency, etc. become important in delivering an AI system. The purpose of this paper is to present a view of a holistic quality management framework for ML applications based on the current advances and identify new areas of software engineering research to achieve a more trustworthy AI.
Convolutional Neural Network (CNN) for Image Classification of Indonesia Sign Language Using Tensorflow

Sign language is a communication system that consists of a set of written symbols or characters for speech and hearing-impaired society to communicate with other people. Understanding sign language is challenging because it requires memorizing hand poses and gestures. This suggests a demand for an automatic sign language recognition system that allows everyone to understand this language. In this research, we have used the Convolutional Neural Network (CNN) Architecture and Tensorflow library to build the model image of classification. Indonesian Sign Language (BISINDO) dataset is used as the data source, which contains 2659 images of Indonesian Sign Language (BISINDO) twenty-six (26) letter categories. The images are divided into training and validation datasets. The experimental results show that the model has achieved an accuracy of 96.67% on the training dataset, and an accuracy of 100% for the validation dataset. In the image classification phase, we uploaded multiple images of alphabet characters and got the result of 100% accuracy for each alphabet character.
A Comparative Study of Deepfake Video Detection Method

Deepfake technology allows humans to manipulate images and videos using deep learning technology. The results from deepfakes are very difficult to distinguish using ordinary vision. Many algorithms are built to detect deepfake content in images and videos. There are several approaches in deepfake detection, including a visual feature-based approach, a local feature-based approach, a deep feature-based approach and a temporal feature-based approach. The main challenge in developing deepfake detection algorithms is the variety of existing deepfake models in both images and videos. Another challenge is that deepfake technology is still evolving, making deepfake images and videos look more realistic and harder to detect.
Research on key Technology of Classroom Teaching Evaluation Based on Artificial Intelligence

Abstract There are many defects in current classroom teaching evaluation, which is not conducive to the implementation of quality education. Therefore, the AI-based classroom teaching evaluation technology is proposed, using artificial intelligence theory, from the perspective of serving quality education, analyzing the problems in teaching evaluation, exploring effective classroom teaching evaluation, and ultimately promoting the development o f students. Starting from the theory of artificial intelligence, it discusses the necessity of constructing a multi-evaluation system for the teaching process, which improves students’ language application ability and humanistic quality, and applies artificial intelligence theory to construct teaching from the aspects of evaluation methods, evaluation methods, evaluation objects, and evaluation subjects. Evaluation System. Experimental results show that this technology can accurately evaluate teaching effects and improve teaching quality
Data transmission by quantum matter wave modulation

Abstract Classical communication schemes exploiting wave modulation are the basis of our information era. Quantum information techniques with photons enable future secure data transfer in the dawn of decoding quantum computers. Here we demonstrate that also matter waves can be applied for secure data transfer. Our technique allows the transmission of a message by a quantum modulation of coherent electrons in a biprism interferometer. The data is encoded in the superposition state by a Wien filter introducing a longitudinal shift between separated matter wave packets. The transmission receiver is a delay line detector performing a dynamic contrast analysis of the fringe pattern. Our method relies on the Aharonov–Bohm effect but does not shift the phase. It is demonstrated that an eavesdropping attack will terminate the data transfer by disturbing the quantum state and introducing decoherence. Furthermore, we discuss the security limitations of the scheme due to the multi-particle aspect and propose the implementation of a key distribution protocol that can prevent active eavesdropping.
Adapting Virtual Embodiment Through Reinforcement Learning

In Virtual Reality, having a virtual body opens a wide range of possibilities as the participant's avatar can appear to be quite different from oneself for the sake of the targeted application (e.g., for perspective-taking). In addition, the system can partially manipulate the displayed avatar movement through some distortion to make the overall experience more enjoyable and effective (e.g., training, exercising, rehabilitation). Despite its potential, an excessive distortion may become noticeable and break the feeling of being embodied into the avatar. Past researches have shown that individuals have a relatively high tolerance to movement distortions and a great variability of individual sensitivities to distortions. In this article, we propose a method taking advantage of Reinforcement Learning (RL) to efficiently identify the magnitude of the maximum distortion that does not get noticed by an individual (further noted the detection threshold). We show through a controlled experiment with subjects that the RL method finds a more robust detection threshold compared to the adaptive staircase method, i.e., it is more able to prevent subjects from detecting the distortion when its amplitude is equal or below the threshold. Finally, the associated majority voting system makes the RL method able to handle more noise within the forced choices input than adaptive staircase. This last feature is essential for future use with physiological signals as these latter are even more susceptible to noise. It would then allow to calibrate embodiment individually to increase the effectiveness of the proposed interactions.
Certificate-Based Parallel Key-Insulated Aggregate Signature Against Fully Chosen Key Attacks for Industrial Internet of Things

With the emergence of the Industrial Internet of Things (IIoT), numerous operations based on smart devices contribute to producing the convenience and comfortable applications for individuals and organizations. Considering the untrusted feature of the communication channels in IIoT, it is essential to ensure the authentication and incontestableness of the messages transmitted in the IIoT. In this article, we first proposed a certificate-based parallel key-insulated aggregate signature (CB-PKIAS), which can resist the fully chosen-key attacks. Concretely, the adversary who can obtain the private keys of all signers in the system is able to forge a valid aggregate signature by using the invalid single signature. Furthermore, our scheme inherits the merits of certificate based and key insulated to avoid the certificate management problem, key-escrow problems, as well as the key exposures simultaneously. In addition, the rigorous analysis and the concrete simulation experiment demonstrated that our proposed scheme is secure under the random oracle and more suitable for the IIoT environment.
AutoClust: A Framework for Automated Clustering Based on Cluster Validity Indices

Automated machine learning (AutoML) aims to minimize human intervention during a machine learning task, for example by means of automatic algorithm selection and its configuration for the data set at hand. Although this research direction has attracted much interest lately, both in academia and industry, existing systems and tools mainly target the domain of supervised learning. However, unsupervised learning, in particular clustering, also calls for AutoML solutions, especially due to the ambiguity involved when evaluating clustering results. Motivated by this shortcoming, in this paper, we introduce a framework for automated clustering that encompasses two main modules: algorithm selection and hyperparameter tuning. Our approach to algorithm selection relies on meta-learning, based on novel meta-features extracted from data sets that attempt to capture similarities in the clustering structure. This approach is coupled with a method for hyperparameter tuning based on Bayesian optimization, where the main novelty is the proposal of an optimization goal that combines different cluster validity indices. We demonstrate the merits of our approach by empirical evaluation on 24 real-life data sets, which shows promising results when compared to existing methods.
Investigating Orphan Transactions in the Bitcoin Network

Orphan transactions are those whose parental income sources are missing at the time that they are processed. These transactions typically languish in a local buffer until they are evicted or all their parents are discovered, at which point they may be propagated further. To date, there has been little work in the literature on characterizing the nature and impact of such orphans, and yet it is intuitive that they should affect the performance of the Bitcoin network. This work thus seeks to methodically research such effects through a measurement campaign on live Bitcoin nodes. Our data show that about 45% of orphan transactions end up being included in the blockchain. Surprisingly, orphan transactions tend to have fewer parents on average than non-orphan transactions, and their missing parents have a lower fee, larger size, and lower transaction fee per byte than all other received transactions. Moreover, the network overhead incurred by these orphan transactions can be significant, exceeding 17% when using the default orphan memory pool size (i.e., 100 transactions), although this overhead can be made negligible, without significant computational or memory demands, if the pool size is simply increased to 1000 transactions. Finally, we show that when a node with an empty mempool first joins the network, 25% of the transactions that it receives become orphan, whereas in steady-state this quantity drops to about 1%.
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks

Deep neural networks (DNNs) are now commonly used in many domains. However, they are vulnerable to adversarial attacks: carefully-crafted perturbations on data inputs that can fool a model into making incorrect predictions. Despite significant research on developing DNN attack and defense techniques, people still lack an understanding of how such attacks penetrate a model's internals. We present Bluff, an interactive system for visualizing, characterizing, and deciphering adversarial attacks on vision-based neural networks. Bluff allows people to flexibly visualize and compare the activation pathways for benign and attacked images, revealing mechanisms that adversarial attacks employ to inflict harm on a model. Bluff is open-sourced and runs in modern web browsers.