Reviewerly/exHarmony huggingface dataset:
Quantum Optical Experiments Modeled by Long Short-Term Memory

We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search but is also an essential step towards automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.
Resource-Constrained Onboard Inference of 3D Object Detection and Localisation in Point Clouds Targeting Self-Driving Applications

Research about deep learning applied in object detection tasks in LiDAR data has been massively widespread in recent years, achieving notable developments, namely in improving precision and inference speed performances. These improvements have been facilitated by powerful GPU servers, taking advantage of their capacity to train the networks in reasonable periods and their parallel architecture that allows for high performance and real-time inference. However, these features are limited in autonomous driving due to space, power capacity, and inference time constraints, and onboard devices are not as powerful as their counterparts used for training. This paper investigates the use of a deep learning-based method in edge devices for onboard real-time inference that is power-effective and low in terms of space-constrained demand. A methodology is proposed for deploying high-end GPU-specific models in edge devices for onboard inference, consisting of a two-folder flow: study model hyperparameters' implications in meeting application requirements; and compression of the network for meeting the board resource limitations. A hybrid FPGA-CPU board is proposed as an effective onboard inference solution by comparing its performance in the KITTI dataset with computer performances. The achieved accuracy is comparable to the PC-based deep learning method with a plus that it is more effective for real-time inference, power limited and space-constrained purposes.
Fully automatic image colorization based on semantic segmentation technology

Aiming at these problems of image colorization algorithms based on deep learning, such as color bleeding and insufficient color, this paper converts the study of image colorization to the optimization of image semantic segmentation, and proposes a fully automatic image colorization model based on semantic segmentation technology. Firstly, we use the encoder as the local feature extraction network and use VGG-16 as the global feature extraction network. These two parts do not interfere with each other, but they share the low-level feature. Then, the first fusion module is constructed to merge local features and global features, and the fusion results are input into semantic segmentation network and color prediction network respectively. Finally, the color prediction network obtains the semantic segmentation information of the image through the second fusion module, and predicts the chrominance of the image based on it. Through several sets of experiments, it is proved that the performance of our model becomes stronger and stronger under the nourishment of the data. Even in some complex scenes, our model can predict reasonable colors and color correctly, and the output effect is very real and natural.
Impact of Customer Compensation Strategies on Outcomes and the Mediating Role of Justice Perceptions: A Longitudinal Study of Target’s Data Breach

Data breaches are a major threat to organizations from both financial and customer relations perspectives. We developed a nomological network linking post-breach compensation strategies to key outcomes, namely continued shopping intentions, positive word-of-mouth, and online complaining, with the effects being mediated by customers’ justice perceptions. We conducted a longitudinal field study investigating Target’s data breach in 2013 that affected more than 110 million customers. We examined customers’ expectations toward compensation immediately after the breach was confirmed (survey 1) and their experiences after reparations were made (survey 2). Evidence from polynomial regression and response surface analyses of data collected from 388 affected customers showed that customers’ justice perceptions were influenced by the actual compensation provided as well as the type and extent of compensation an organization could and should have provided (i.e., customers’ compensation expectations). Interestingly, both positive and negative expectation disconfirmation led to less favorable justice perceptions compared to when expectations were met. Justice perceptions were, in turn, associated with key outcomes. We discuss implications for research on data security, information systems, and justice theory.
Toward Scalable and Unified Example-Based Explanation and Outlier Detection

When neural networks are employed for high-stakes decision-making, it is desirable that they provide explanations for their prediction in order for us to understand the features that have contributed to the decision. At the same time, it is important to flag potential outliers for in-depth verification by domain experts. In this work we propose to unify two differing aspects of explainability with outlier detection. We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction and at the same time identify regions of similarity between the predicted sample and the examples. The examples are real prototypical cases sampled from the training set via a novel iterative prototype replacement algorithm. Furthermore, we propose to use the prototype similarity scores for identifying outliers. We compare performance in terms of the classification, explanation quality and outlier detection of our proposed network with baselines. We show that our prototype-based networks extending beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy.
A survey on textual entailment based question answering

Question answering, an information retrieval system that seeks knowledge, is one of the classic applications in Natural Language Processing. A question answering system comprises numerous sets of subtasks. Some of the subtasks are Passage Retrieval, Answer Ranking, Question Similarity, Question Generation, Question Classification, Answer Selection, and Answer Validation. Numerous approaches have been experimented on in the question answering system to achieve accurate results. One such approach for the question answering system is Textual Entailment. Textual Entailment is a framework that captures significant semantic inference. Textual Entailment of two text fragments can be defined as the task of deciding whether the meaning of one text fragment can be inferred from another text fragment. This survey discusses how and why Textual Entailment is applied to various subtasks in question answering.
A modified genetic algorithm for system optimization

Purpose In this paper, the previously developed idea of generalized optimization of circuits for deterministic methods has been extended to genetic algorithm (GA) to demonstrate new possibilities for solving an optimization problem that enhance accuracy and significantly reduce computing time. Design/methodology/approach The disadvantages of GAs are premature convergence to local minima and an increase in the computer operation time when setting a sufficiently high accuracy for obtaining the minimum. The idea of generalized optimization of circuits, previously developed for the methods of deterministic optimization, is built into the GA and allows one to implement various optimization strategies based on GA. The shape of the fitness function, as well as the length and structure of the chromosomes, is determined by a control vector artificially introduced within the framework of generalized optimization. This study found that changing the control vector that determines the method for calculating the fitness function makes it possible to bypass local minima and find the global minimum with high accuracy and a significant reduction in central processing unit (CPU) time. Findings The structure of the control vector is found, which makes it possible to reduce the CPU time by several orders of magnitude and increase the accuracy of the optimization process compared with the traditional approach for GAs. Originality/value It was demonstrated that incorporating the idea of generalized optimization into the body of a stochastic optimization method leads to qualitatively new properties of the optimization process, increasing the accuracy and minimizing the CPU time.
Jump-Starting Item Parameters for Adaptive Language Tests

A challenge in designing high-stakes language assessments is calibrating the test item difficulties, either a priori or from limited pilot test data.While prior work has addressed 'cold start' estimation of item difficulties without piloting, we devise a multi-task generalized linear model with BERT features to jump-start these estimates, rapidly improving their quality with as few as 500 test-takers and a small sample of item exposures (≈6 each) from a large item bank (≈4,000 items).Our joint model provides a principled way to compare test-taker proficiency, item difficulty, and language proficiency frameworks like the Common European Framework of Reference (CEFR).This also enables new item difficulty estimates without piloting them first, which in turn limits item exposure and thus enhances test security.Finally, using operational data from the Duolingo English Test, a high-stakes English proficiency test, we find that difficulty estimates derived using this method correlate strongly with lexico-grammatical features that correlate with reading complexity.* Research conducted during an internship at Duolingo. C1C2 A1 A2 B1 B2
On the space-time statistics of motion pictures

It is well known that natural images possess statistical regularities that can be captured by bandpass decomposition and divisive normalization processes that approximate early neural processing in the human visual system. We expand on these studies and present new findings on the properties of space-time natural statistics that are inherent in motion pictures. Our model relies on the concept of temporal bandpass (e.g., lag) filtering in lateral geniculate nucleus (LGN) and area V1, which is similar to smoothed frame differencing of video frames. Specifically, we model the statistics of the differences between adjacent or neighboring video frames that have been slightly spatially displaced relative to one another. We find that when these space-time differences are further subjected to locally pooled divisive normalization, statistical regularities (or lack thereof) arise that depend on the local motion trajectory. We find that bandpass and divisively normalized frame differences that are displaced along the motion direction exhibit stronger statistical regularities than for other displacements. Conversely, the direction-dependent regularities of displaced frame differences can be used to estimate the image motion (optical flow) by finding the space-time displacement paths that best preserve statistical regularity.
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records

Disease risk prediction has attracted increasing attention in the field of modern healthcare, especially with the latest advances in artificial intelligence (AI).Electronic health records (EHRs), which contain heterogeneous patient information, are widely used in disease risk prediction tasks.One challenge of applying AI models for risk prediction lies in generating interpretable evidence to support the prediction results while retaining the prediction ability.In order to address this problem, we propose the method of jointly embedding words and labels whereby attention modules learn the weights of words from medical notes according to their relevance to the names of risk prediction labels.This approach boosts interpretability by employing an attention mechanism and including the names of prediction tasks in the model.However, its application is only limited to the handling of textual inputs such as medical notes.In this paper, we propose a label dependent attention model LDAM to 1) improve the interpretability by exploiting Clinical-BERT (a biomedical language model pre-trained on a large clinical corpus) to encode biomedically meaningful features and labels jointly; 2) extend the idea of joint embedding to the processing of timeseries data, and develop a multi-modal learning framework for integrating heterogeneous information from medical notes and time-series health status indicators.To demonstrate our method, we apply LDAM to the MIMIC-III dataset to predict different disease risks.We evaluate our method both quantitatively and qualitatively.Specifically, the predictive power of LDAM will be shown, and case studies will be carried out to illustrate its interpretability.
A Selective Segmentation Model Using Dual-Level Set Functions and Local Spatial Distance

Selective image segmentation is one of the most significant subjects in medical imaging and real-world applications. We present a robust selective segmentation model based on local spatial distance utilizing a dual-level set variational formulation in this study. Our concept tries to partition all objects using a global level set function and the selected item using a different level set function (local). Our model combines the marker distance function, edge detection, local spatial distance, and active contour without edges into one. The new model is robust to noise and gives better performance for images having intensity in-homogeneity (background and foreground). Moreover, we observed that the proposed model captures objects which do not have uniform features. The experimental results show that our model is robust to noise and works better than the other existing models.
Tracking Eye Movement Using a Composite Magnet

Tracking blinking and eye movements has many applications in automotive, gaming, sleep laboratories, controlling machines, and tracking our health. The current technologies are uncomfortable to use, invasive, and require the attention of the user, specialized training, or the eyes to be open all the time. With the advancements in wearable electronics, realizing a comfortable eye tracker for everyday use is now more feasible than ever. This work presents a magnetic eye tracker achieved by attaching a composite magnet to the eyelid. A multi-axis magnetic field sensor, which is placed in a comfortable nearby location (i.e., integrated into a sleeping mask), detects the motion of the composite magnet due to the movement of the eyeball. Thus, the system is capable of measuring when the eyeball moves down, up, left, or right. It is wearable, battery-powered, and completely wireless, allowing for comfortable everyday use. To facilitate the continuous tracking of eye movement, a software component is developed in which a MATLAB code analyzes the systematic changes on the measured magnetic field at the tracker. Accordingly, the system marks when the eye moves down/up or left/right.
Blockchain abnormal behavior awareness methods: a survey

Abstract With the wide application and development of blockchain technology in various fields such as finance, government affairs and medical care, security incidents occur frequently on it, which brings great threats to users’ assets and information. Many researchers have worked on blockchain abnormal behavior awareness in respond to these threats. We summarize respectively the existing public blockchain and consortium blockchain abnormal behavior awareness methods and ideas in detail as the difference between the two types of blockchain. At the same time, we summarize and analyze the existing data sets related to mainstream blockchain security, and finally discuss possible future research directions. Therefore, this work can provide a reference for blockchain security awareness research.
A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics

The rapid evolution of industrial components, the paradigm of Industry 4.0, and the new connectivity features introduced by 5G technology all increase the likelihood of cybersecurity incidents. Such incidents are caused by the vulnerabilities present in these components. Designing a secure system is critical, but it is also complex, costly, and an extra factor to manage during the lifespan of the component. This paper presents a model to analyze the known vulnerabilities of industrial components over time. The proposed Extended Dependency Graph (EDG) model is based on two main elements: a directed graph representation of the internal structure of the component, and a set of quantitative metrics based on the Common Vulnerability Scoring System (CVSS). The EDG model can be applied throughout the entire lifespan of a device to track vulnerabilities, identify new requirements, root causes, and test cases. It also helps prioritize patching activities. The model was validated by application to the OpenPLC project. The results reveal that most of the vulnerabilities associated with OpenPLC were related to memory buffer operations and were concentrated in the libssl library. The model was able to determine new requirements and generate test cases from the analysis.
A new benchmark harness for systematic and robust evaluation of streaming state stores

Modern stream processing systems often rely on embedded key-value stores, like RocksDB, to manage the state of long-running computations. Evaluating the performance of these stores when used for streaming workloads is cumbersome as it requires the configuration and deployment of a stream processing system that integrates the respective store, and the execution of representative queries to collect measurements.
Bubble detection in Bitcoin and Ethereum and its relationship with volatility regimes

Purpose The aim of this paper is to test the existence of bubbles for the daily prices of cryptocurrencies Bitcoin and Ethereum and verify if there is a relationship between bubbles and volatility regimes. Design/methodology/approach The authors test the presence of bubbles with the generalized supremum augmented Dickey–Fuller (GSADF) test using critical values simulated by the bootstrap procedures of Gutierrez (2011), Harvey et al. (2016) and Pedersen and Schütte (2020). Also, the authors estimate Markov regime switching generalized autoregressive conditional heteroskedasticity model for these cryptocurrencies. Findings The GSADF test result indicates the presence of bubbles for both cryptocurrencies. Simulating critical values by wild-bootstrap, which is robust to non-stationary volatility, leads to the highest number of bubbles in both cryptocurrencies. In addition, based on the estimates of conditional variance models with regime changes, the authors find that the bubbles identified are associated with a regime of low returns volatility, indicating a change in the trade-off between risk and return when the prices of cryptocurrencies differ from their fundamental values. Originality/value To the best of the authors knowledge, there are no studies that test the explosive behavior for cryptocurrencies by the GSADF test using the bootstrap method to simulate critical values from the procedures of Harvey et al. (2016) or Pedersen and Schütte (2020). These bootstrapping procedures are robust to heteroscedasticity and avoid the detection of false bubbles. Further, the advantage of Harvey et al. (2016) procedure is the robustness to non-stationary volatility.
Psychological constructs as local optima

Psychological constructs are necessary abstractions to operationalize otherwise intractable entities. However, the way constructs are defined and refined over time introduces notable bias into models of behaviour, which prevents effective knowledge building within and across subfields. Psychological constructs are necessary abstractions to operationalize otherwise intractable entities. However, the way constructs are defined and refined over time introduces notable bias into models of behaviour, which prevents effective knowledge building within and across subfields.
TS-YOLO:An efficient YOLO Network for Multi-scale Object Detection

To solve the problem that the You Only Look Once(YOLO) v4 still has missing detection in multi-scale object detection, we proposed a novel deep convolutional network structure TS-YOLO with three spatial pyramid pooling(SPP) modules in YOLOv4. SPP plays an important role in multi-scale object detection, it can extract more semantic information in complex scenes. In this paper, we add two more SPP modules and redesign the pooling core sizes in SPP on the basis of YOLOv4. Our training was on the Pascal VOC data set and the experimental results show that our TS-YOLO not only detects more objects but also has 2.21% higher accuracy compared with original YOLOv4, demonstrating the excellent performance of our model in multi-scale object detection.
Benchmarks for solar radiation time series forecasting

With an ever-increasing share of intermittent renewable energy in the world's energy mix,there is an increasing need for advanced solar power forecasting models to optimize the operation and control of solar power plants. In order to justify the need for more elaborate forecast modeling, one must compare the performance of advanced models with naive reference methods. On this point, a rigorous formalism using statistical tools, variational calculation and quantification of noise in the measurement is studied and five naive reference forecasting methods are considered, among which there is a newly proposed approach called ARTU (a particular autoregressive model of order two). These methods do not require any training phase nor demand any (or almost no) historical data. Additionally, motivated by the well-known benefits of ensemble forecasting, a combination of these models is considered, and then validated using data from multiple sites with diverse climatological characteristics, based on various error metrics, among which some are rarely used in the field of solar energy. The most appropriate benchmarking method depends on the salient features of the variable being forecast (e.g., seasonality, cyclicity, or conditional heteoroscedasity) as well as the forecast horizon. Hence, to ensure a fair benchmarking, forecasters should endeavor to discover the most appropriate naive reference method for their setup by testing all available options. Among the methods proposed in this paper, the combination and ARTU statistically offer the best results for the proposed study conditions.