Reviewerly/exHarmony huggingface dataset:
Coherent interaction-free detection of microwave pulses with a superconducting circuit

The interaction-free measurement is a fundamental quantum effect whereby the presence of a photosensitive object is determined without irreversible photon absorption. Here we propose the concept of coherent interaction-free detection and demonstrate it experimentally using a three-level superconducting transmon circuit. In contrast to standard interaction-free measurement setups, where the dynamics involves a series of projection operations, our protocol employs a fully coherent evolution that results, surprisingly, in a higher probability of success. We show that it is possible to ascertain the presence of a microwave pulse resonant with the second transition of the transmon, while at the same time avoid exciting the device onto the third level. Experimentally, this is done by using a series of Ramsey microwave pulses coupled into the first transition and monitoring the ground-state population.
Bu-Dash: a universal and dynamic graphical password scheme (extended version)

Abstract Passwordless authentication is a trending theme in cyber security, while biometrics gradually replace knowledge-based schemes. However, Personal Identification Numbers, passcodes, and graphical passwords are still considered as the primary means for authentication. Passwords must be memorable to be usable; therefore, users tend to choose easy to guess secrets, compromising security. The Android Pattern Unlock is a popular graphical password scheme that can be easily attacked by exploiting human behavioristic traits. Despite its vulnerabilities, the popularity of the scheme has led researchers to propose adjustments and variations that enhance security but maintain its familiar user interface. Nevertheless, prior work demonstrated that improving security while preserving usability remains frequently a hard task. In this paper we propose a novel graphical password scheme built on the foundations of the well-accepted Android Pattern Unlock method, which is usable, inclusive, universal, and robust against shoulder surfing and (basically) smudge attacks. Our scheme, named , features a dynamic user interface that mutates every time a user swipes the screen. Our pilot studies illustrate that attracts positive user acceptance rates, it is secure, and maintains high usability levels. We define complexity metrics that can be used to further diversify user input, and we conduct complexity and security assessments.
Question Guru: An Automated Multiple-Choice Question Generation System

During the last two decades, natural language processing (NLP) puts a tremendous impact on automated text generation. There are various important libraries in NLP that aid in the development of advanced applications in a variety of sectors, most notably education, with a focus on learning and assessment. In the learning environment, objective evaluation is a common approach to assessing student performance. Multiple-choice questions (MCQs) are a popular form of evaluation and self-assessment in both traditional and electronic learning contexts. A system that generates multiple-choice questions automatically would be extremely beneficial to teachers. The objective of this study is to develop an NLP based system, Quru (Question Guru), to produce questions automatically from text content. The Quru is broken into three basic steps to construct an automated MCQs generation system: Stem Extraction (Important Sentences Selection), Keyword Extraction, and Distractor Generation. Furthermore, the system's performance is validated by university lecturers. As per the findings, the MCQs generated are more than 80% accurate.
A hybridized channel selection approach with deep convolutional neural network for effective ovarian cancer prediction in periodic acid‐Schiff‐stained images

Summary In today's world, cancers are becoming a crucial warning in current medical applications where they show a significant part in the prognosis and appraisal of ovarian malignancies in histopathological imaging. Automated approaches to systematize the formation and categorization of cancers in periodic acid‐Schiff (PAS)‐stained images have recently been quickly growing in the area of digital pathology. However, the existing literature lacks computerized approaches to systematize the localization and categorization of cancers in PAS‐stained images. In this work, a new deep fully connected convolutional neural network (DFCNN) along with hybridized channel selection (HCS) strategy has been proposed to diagnose ovarian cancers accurately in PAS‐stained images. Primarily, the HCS strategy selects the optimal channel for managing the input dataset. Afterward, the DFCNN is employed to extract the influential features from the PAS‐stained images. The autoencoder modeling can be carried out to build the proposed DFCNN layers. The appropriate layer selection in the proposed model facilitates to obtaining accurate cancer classification with minimal misclassification errors. The performance results manifest that the proposed model achieves a superior accuracy of 99.22% when compared with existing CNN+softmax, CNN+decision tree, and CNN+radial basis function models.
SpotLake: Diverse Spot Instance Dataset Archive Service

Public cloud service vendors provide a surplus of computing resources at a cheaper price as a spot instance. Despite the cheaper price, the spot instance can be forced to be shutdown at any moment whenever the surplus resources are in shortage. To enhance spot instance usage, vendors provide diverse spot instance datasets. Among them, the spot price information has been most widely used so far. However, the tendency toward barely changing spot price [5], [21] weakens the applicability of the spot price dataset. Besides the price dataset, the recently introduced spot instance availability and interruption ratio datasets can help users better utilize spot instances, but they are rarely used in reality. With a thorough analysis, we could uncover major hurdles when using the new datasets concerning the lack of historical information, query constraints, and limited query interfaces. To overcome them, we develop SpotLake, a spot instance data archive web service that provides historical information of various spot instance datasets. Novel heuristics to collect various datasets and a data serving architecture are presented. Through real-world spot instance availability experiments, we present the applicability of the proposed system. SpotLake is publicly available as a web service to speed up cloud system research to improve spot instance usage and availability while reducing cost.
On the First-Order Optimization Methods in Deep Image Prior

Abstract Deep learning methods have state-of-the-art performances in many image restoration tasks. Their effectiveness is mostly related to the size of the dataset used for the training. Deep image prior (DIP) is an energy-function framework which eliminates the dependency on the training set, by considering the structure of a neural network as an handcrafted prior offering high impedance to noise and low impedance to signal. In this paper, we analyze and compare the use of different optimization schemes inside the DIP framework for the denoising task.
Quantum approximate optimization algorithm for Bayesian network structure learning

Bayesian network structure learning is an NP-hard problem that has been faced by a number of traditional approaches in recent decades. Currently, quantum technologies offer a wide range of advantages that can be exploited to solve optimization tasks that cannot be addressed in an efficient way when utilizing classic computing approaches. In this work, a specific type of variational quantum algorithm, the quantum approximate optimization algorithm, was used to solve the Bayesian network structure learning problem, by employing $$3n(n-1)/2$$ qubits, where n is the number of nodes in the Bayesian network to be learned. Our results showed that the quantum approximate optimization algorithm approach offers competitive results with state-of-the-art methods and quantitative resilience to quantum noise. The approach was applied to a cancer benchmark problem, and the results justified the use of variational quantum algorithms for solving the Bayesian network structure learning problem.
Analyzing neural network behavior through deep statistical model checking

Abstract Neural networks (NN) are taking over ever more decisions thus far taken by humans, even though verifiable system-level guarantees are far out of reach. Neither is the verification technology available, nor is it even understood what a formal, meaningful, extensible, and scalable testbed might look like for such a technology. The present paper is an attempt to improve on both the above aspects. We present a family of formal models that contain basic features of automated decision-making contexts and which can be extended with further orthogonal features, ultimately encompassing the scope of autonomous driving. Due to the possibility to model random noise in the decision actuation, each model instance induces a Markov decision process (MDP) as verification object. The NN in this context has the duty to actuate (near-optimal) decisions. From the verification perspective, the externally learnt NN serves as a determinizer of the MDP, the result being a Markov chain which as such is amenable to statistical model checking. The combination of an MDP and an NN encoding the action policy is central to what we call “deep statistical model checking” (DSMC). While being a straightforward extension of statistical model checking, it enables to gain deep insight into questions like “how high is the NN-induced safety risk?”, “how good is the NN compared to the optimal policy?” (obtained by model checking the MDP), or “does further training improve the NN?”. We report on an implementation of DSMC inside the Modest Toolset in combination with externally learnt NNs, demonstrating the potential of DSMC on various instances of the model family, and illustrating its scalability as a function of instance size as well as other factors like the degree of NN training.
Global reconstruction of language models with linguistic rules – Explainable AI for online consumer reviews

Abstract Analyzing textual data by means of AI models has been recognized as highly relevant in information systems research and practice, since a vast amount of data on eCommerce platforms, review portals or social media is given in textual form. Here, language models such as BERT, which are deep learning AI models, constitute a breakthrough and achieve leading-edge results in many applications of text analytics such as sentiment analysis in online consumer reviews. However, these language models are “black boxes”: It is unclear how they arrive at their predictions. Yet, applications of language models, for instance, in eCommerce require checks and justifications by means of global reconstruction of their predictions, since the decisions based thereon can have large impacts or are even mandatory due to regulations such as the GDPR. To this end, we propose a novel XAI approach for global reconstructions of language model predictions for token-level classifications (e.g., aspect term detection) by means of linguistic rules based on NLP building blocks (e.g., part-of-speech). The approach is analyzed on different datasets of online consumer reviews and NLP tasks. Since our approach allows for different setups, we further are the first to analyze the trade-off between comprehensibility and fidelity of global reconstructions of language model predictions. With respect to this trade-off, we find that our approach indeed allows for balanced setups for global reconstructions of BERT’s predictions. Thus, our approach paves the way for a thorough understanding of language model predictions in text analytics. In practice, our approach can assist businesses in their decision-making and supports compliance with regulatory requirements.
Low-Light Image Enhancement with Contrast Increase and Illumination Smooth

In image enhancement, maintaining the texture and attenuating noise are worth discussing. To address these problems, we propose a low-light image enhancement method with contrast increase and illumination smooth. First, we calculate the maximum map and the minimum map of RGB channels, and then we set maximum map as the initial value for illumination and introduce minimum map to smooth illumination. Second, we use the histogram-equalized version of the input image to construct the weight for the illumination map. Third, we propose an optimization problem to obtain the smooth illumination and refined reflectance. Experimental results show that our method can achieve better performance compared to the state-of-the-art methods.
A New Orientation Detection Method for Tilting Insulators Incorporating Angle Regression and Priori Constraints

The accurate detection of insulators is an important prerequisite for insulator fault diagnosis. To solve the problem of background interference and overlap caused by the axis-aligned bounding boxes in the tilting insulator detection tasks, we construct an improved detection architecture according to the scale and tilt features of the insulators from several perspectives, such as bounding box representation, loss function, and anchor box construction. A new orientation detection method for tilting insulators based on angle regression and priori constraints is put forward in this paper. Ablation tests and comparative validation tests were conducted on a self-built aerial insulator image dataset. The results show that the detection accuracy of our model was increased by 7.98% compared with that of the baseline, and the overall detection accuracy reached 82.33%. Moreover, the detection effect of our method was better than that of the YOLOv5 detection model and other orientation detection models. Our model provides a new idea for the accurate orientation detection of insulators.
Multi-UAV Path Planning Algorithm Based on BINN-HHO

Multi-UAV (multiple unmanned aerial vehicles) flying in three-dimensional (3D) mountain environments suffer from low stability, long-planned path, and low dynamic obstacle avoidance efficiency. Spurred by these constraints, this paper proposes a multi-UAV path planning algorithm that consists of a bioinspired neural network and improved Harris hawks optimization with a periodic energy decline regulation mechanism (BINN-HHO) to solve the multi-UAV path planning problem in a 3D space. Specifically, in the procession of global path planning, an energy cycle decline mechanism is introduced into HHO and embed it into the energy function, which balances the algorithm's multi-round dynamic iteration between global exploration and local search. Additionally, when the onboard sensors detect a dynamic obstacle during the flight, the improved BINN algorithm conducts a local path replanning for dynamic obstacle avoidance. Once the dynamic obstacles in the sensor detection area disappear, the local path planning is completed, and the UAV returns to the trajectory determined by the global planning. The simulation results show that the proposed Harris hawks algorithm has apparent superiorities in path planning and dynamic obstacle avoidance efficiency compared with the basic Harris hawks optimization, particle swarm optimization (PSO), and the sparrow search algorithm (SSA).
HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry

The immunohistochemical (IHC) evaluation of epidermal growth factor 2 (HER2) for the diagnosis of breast cancer is still qualitative with a high degree of inter-observer variability, and thus requires the incorporation of complementary techniques such as fluorescent in situ hybridization (FISH) to resolve the diagnosis. Implementing automatic algorithms to classify IHC biomarkers is crucial for typifying the tumor and deciding on therapy for each patient with better performance. The present study aims to demonstrate that, using an explainable Machine Learning (ML) model for the classification of HER2 photomicrographs, it is possible to determine criteria to improve the value of IHC analysis. We trained a logistic regression-based supervised ML model with 393 IHC microscopy images from 131 patients, to discriminate between upregulated and normal expression of the HER2 protein. Pathologists' diagnoses (IHC only) vs. the final diagnosis complemented with FISH (IHC + FISH) were used as training outputs. Basic performance metrics and receiver operating characteristic curve analysis were used together with an explainability algorithm based on Shapley Additive exPlanations (SHAP) values to understand training differences. The model could discriminate amplified IHC from normal expression with better performance when the training output was the IHC + FISH final diagnosis (IHC vs. IHC + FISH: area under the curve, 0.94 vs. 0.81). This may be explained by the increased analytical impact of the membrane distribution criteria over the global intensity of the signal, according to SHAP value interpretation. The classification model improved its performance when the training input was the final diagnosis, downplaying the weighting of the intensity of the IHC signal, suggesting that to improve pathological diagnosis before FISH consultation, it is necessary to emphasize subcellular patterns of staining.
How A 360° virtual tour is more effective than photographs on communication effects: the roles of mental imagery processing and a sense of presence

The study aims to investigate how a 360° virtual tour is more effective than photographs in strengthening viewers’ attitudes when promoting tourist destinations. The results of a quasi-experiment indicate that a 360° virtual tour led to higher mental imagery processing in the dimensions of quantity, modality, and valence; however, there was no significant difference in the dimension of vividness. These four dimensions of mental imagery have a significant impact on the sense of presence and communication effects (attitude strength and attitude confidence). In addition, a sense of presence mediates the relationship between mental imagery processing and communication effects.
Duplicate Bug Report Detection: How Far Are We?

Many Duplicate Bug Report Detection (DBRD) techniques have been proposed in the research literature. The industry uses some other techniques. Unfortunately, there is insufficient comparison among them, and it is unclear how far we have been. This work fills this gap by comparing the aforementioned techniques. To compare them, we first need a benchmark that can estimate how a tool would perform if applied in a realistic setting today. Thus, we first investigated potential biases that affect the fair comparison of the accuracy of DBRD techniques. Our experiments suggest that data age and issue tracking system (ITS) choice cause a significant difference. Based on these findings, we prepared a new benchmark. We then used it to evaluate DBRD techniques to estimate better how far we have been. Surprisingly, a simpler technique outperforms recently proposed sophisticated techniques on most projects in our benchmark. In addition, we compared the DBRD techniques proposed in research with those used in Mozilla and VSCode . Surprisingly, we observe that a simple technique already adopted in practice can achieve comparable results as a recently proposed research tool. Our study gives reflections on the current state of DBRD, and we share our insights to benefit future DBRD research.
Research on local real-time obstacle avoidance path planning of unmanned vehicle based on improved artificial potential field method

According to the requirements of real-time obstacle avoidance of unmanned vehicles, an improved artificial potential field local obstacle avoidance path planning algorithm is proposed. Considering the influence factors of vehicle kinematics and dynamics, an improved distance adjustment factor is added to the static obstacle potential field function to construct the water drop repulsion potential field, which improves the efficiency of path planning; The relative velocity function and relative acceleration function are added to the potential field function of dynamic obstacles, which solves the problem that the traditional potential field method has no solution under the dynamic obstacle avoidance condition. The Bessel curve is used to smooth the path, and finally a fast, efficient and collision-free optimal path is generated. Using the Prescan-CarSim-Matlab/Simulink joint simulation platform, the effectiveness of the proposed path planning algorithm is verified under the conditions of lane changing obstacle avoidance of static obstacles and deceleration obstacle avoidance of lateral dynamic obstacles. The simulation results show that compared with the traditional potential field method, the change of heading angle obtained by the improved potential field method is reduced by 84% and the stability of dynamic obstacle avoidance is improved.
Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

Abstract Path planning is a key research issue in the field of unmanned aerial vehicle (UAV) applications. In practical applications, multi-objective path planning is usually required for multi-UAVs, so this paper proposes the improved balanced artificial bee colony (IB-ABC) algorithm to optimize multi-objective path planning. The algorithm adopts the ABC algorithm that emphasizes the global search capability, which is based on iterative feedback information. It uses single-element points, multi-element points, and iteration constraints to optimize the strategies of employed bees, follower bees, and scout bees, respectively. In terms of time and priority, simulation experiments prove that the IB-ABC algorithm can balance local and global search capabilities, accelerate the speed of convergence, and realize multi-UAV path planning in complex mountain environments.
LGAN-DP: A novel differential private publication mechanism of trajectory data

With the popularity of mobile Internet, big data of trajectory not only provides convenience for people, but also brings privacy leakage. In order to overcome the problems in the current works such as insufficient accuracy, insufficient efficiency and insufficient protection of trajectory data privacy in trajectory data publishing methods, a trajectory data differential privacy publishing mechanism (LGAN-DP) based on LSTM-GAN is proposed in this paper, which uses a long short-term memory network and a generative adversarial network to generate synthetic trajectories. We design a trajectory loss function to judge the trajectory similarity loss of the synthetic trajectories trained by the trajectory data deep learning model proposed in this paper. LGAN-DP processes the result set of trajectory data publishing by using differential privacy. The experimental results show that LGAN-DP can better guarantee the balance between the privacy and availability of trajectory data on the MI. The availability of trajectory data is improved by 40%∼50% as compared with the existing methods, with less time complexity.