Reviewerly/exHarmony huggingface dataset:
Neural predictive monitoring and a comparison of frequentist and Bayesian approaches

Abstract Neural state classification (NSC) is a recently proposed method for runtime predictive monitoring of hybrid automata (HA) using deep neural networks (DNNs). NSC trains a DNN as an approximate reachability predictor that labels an HA state x as positive if an unsafe state is reachable from x within a given time bound, and labels x as negative otherwise. NSC predictors have very high accuracy, yet are prone to prediction errors that can negatively impact reliability. To overcome this limitation, we present neural predictive monitoring (NPM), a technique that complements NSC predictions with estimates of the predictive uncertainty. These measures yield principled criteria for the rejection of predictions likely to be incorrect, without knowing the true reachability values. We also present an active learning method that significantly reduces the NSC predictor’s error rate and the percentage of rejected predictions. We develop two versions of NPM based, respectively, on the use of frequentist and Bayesian techniques to learn the predictor and the rejection rule. Both versions are highly efficient, with computation times on the order of milliseconds, and effective, managing in our experimental evaluation to successfully reject almost all incorrect predictions. In our experiments on a benchmark suite of six hybrid systems, we found that the frequentist approach consistently outperforms the Bayesian one. We also observed that the Bayesian approach is less practical, requiring a careful and problem-specific choice of hyperparameters.
Quantum approximate optimization algorithm with adaptive bias fields

The quantum approximate optimization algorithm (QAOA) transforms a simple many-qubit wave function into one that encodes a solution to a difficult classical optimization problem. It does this by optimizing the schedule according to which two unitary operators are alternately applied to the qubits. In this paper, the QAOA is modified by updating the operators themselves to include local fields, using information from the measured wave function at the end of one iteration step to improve the operators at later steps. It is shown by numerical simulation on MaxCut problems that, for a fixed accuracy, this procedure decreases the runtime of QAOA very substantially. This improvement appears to increase with the problem size. Our method requires essentially the same number of quantum gates per optimization step as the standard QAOA, and no additional measurements. This modified algorithm enhances the prospects for quantum advantage for certain optimization problems.
Machine Learning Applications for Computer-Aided Medical Diagnostics

Machine learning has made potential developments in biotechnology. Years of medical training are required for correct diagnosis of diseases. Diagnostics is often a very time-consuming process, and it requires strenuous effort. Data generated through varieties of imaging modalities for the diagnoses purpose is very bulky. In the corporate and government hospitals, a high number of patients are visiting per day for the disease diagnosis and treatment. This may cause diagnosis burden on the clinicians and radiologist. For interpretation, overload of image data may produce oversight and observational errors. Machine learning algorithms have recently made huge advancements in automated disease detection and classification. These algorithms can learn to view the patterns in an image similarly the way doctors do by training those using lots of annotated examples. Various machine learning algorithms used for automated diagnosis in medical imaging filed are discussed in the paper. Comparative analysis of these algorithms based on different parameters is also presented. This paper also focused at various applications of machine learning in diagnostic imaging, which can be part of routine clinical work for detection and classification of the process.
A Theory of Scrum Team Effectiveness

Scrum teams are at the heart of the Scrum framework. Nevertheless, an integrated and systemic theory that can explain what makes some Scrum teams more effective than others is still missing. To address this gap, we performed a 7-year-long mixed-methods investigation composed of two main phases. First, we induced a theoretical model from 13 exploratory field studies. Our model proposes that the effectiveness of Scrum teams depends on five high-level factors (responsiveness, stakeholder concern, continuous improvement, team autonomy, and management support) and 13 lower-level factors. In the second phase of our study, we validated our model with a covariance-based structural equation modeling analysis using data from about 5,000 developers and 2,000 Scrum teams that we gathered with a custom-built survey. Results suggest a very good fit of the empirical data in our theoretical model ( CFI = 0.959, RMSEA = 0.038, SRMR = 0.035). Accordingly, this research allowed us to (1) propose and validate a generalizable theory for effective Scrum teams and (2) formulate clear recommendations for how organizations can better support Scrum teams.
Experimental demonstration of entanglement-enabled universal quantum cloning in a circuit

No-cloning theorem forbids perfect cloning of an unknown quantum state. A universal quantum cloning machine (UQCM), capable of producing two copies of any input qubit with the optimal fidelity, is of fundamental interest and has applications in quantum information processing. This is enabled by delicately tailored nonclassical correlations between the input qubit and the copying qubits, which distinguish the UQCM from a classical counterpart, but whose experimental demonstrations are still lacking. We here implement the UQCM in a superconducting circuit, and investigate these correlations. The measured entanglements well agree with our theoretical prediction that they are independent of the input state and thus constitute a universal quantum behavior of the UQCM that was not previously revealed. Another feature of our experiment is the realization of deterministic and individual cloning, in contrast to previously demonstrated UQCMs, which either were probabilistic or did not constitute true cloning of individual qubits.
ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

Abstract In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.
Predicting CPU usage for proactive autoscaling

Private and public clouds require users to specify requests for resources such as CPU and memory (RAM) to be provisioned for their applications. The values of these requests do not necessarily relate to the application's run-time requirements, but only help the cloud infrastructure resource manager to map requested resources to physical resources. If an application exceeds these values, it might be throttled or even terminated. As a consequence, requested values are often overestimated, resulting in poor resource utilization in the cloud infrastructure. Autoscaling is a technique used to overcome these problems.
Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition

Wearable sensor based human activity recognition is a challenging problem due to difficulty in modeling spatial and temporal dependencies of sensor signals. Recognition models in closed-set assumption are forced to yield members of known activity classes as prediction. However, activity recognition models can encounter an unseen activity due to body-worn sensor malfunction or disability of the subject performing the activities. This problem can be addressed through modeling solution according to the assumption of open-set recognition. Hence, the proposed self attention based approach combines data hierarchically from different sensor placements across time to classify closed-set activities and it obtains notable performance improvement over state-of-the-art models on five publicly available datasets. The decoder in this autoencoder architecture incorporates self-attention based feature representations from encoder to detect unseen activity classes in open-set recognition setting. Furthermore, attention maps generated by the hierarchical model demonstrate explainable selection of features in activity recognition. We conduct extensive leave one subject out validation experiments that indicate significantly improved robustness to noise and subject specific variability in body-worn sensor signals. The source code is available at: github.com/saif-mahmud/hierarchical-attention-HAR.
Counterfactual Thinking: What Theories <i>Do</i> in Design

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Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation

Recent studies imply that deep neural networks are vulnerable to adversarial examples, i.e., inputs with a slight but intentional perturbation are incorrectly classified by the network. Such vulnerability makes it risky for some security-related applications (e.g., semantic segmentation in autonomous cars) and triggers tremendous concerns on the model reliability. For the first time, we comprehensively evaluate the robustness of existing UDA methods and propose a robust UDA approach. It is rooted in two observations: i) the robustness of UDA methods in semantic segmentation remains unexplored, which poses a security concern in this field; and ii) although commonly used self-supervision (e.g., rotation and jigsaw) benefits model robustness in classification and recognition tasks, they fail to provide the critical supervision signals that are essential in semantic segmentation. These observations motivate us to propose adversarial self-supervision UDA (or ASSUDA) that maximizes the agreement between clean images and their adversarial examples by a contrastive loss in the output space. Extensive empirical studies on commonly used benchmarks demonstrate that ASSUDA is resistant to adversarial attacks.
Quantum nonlocality without entanglement: explicit dependence on prior probabilities of nonorthogonal mirror-symmetric states

Abstract In the case of a multi-party system, through local operations and classical communication (LOCC), each party may not perform perfect discrimination of quantum states that are separable and orthogonal. This property of quantum ensemble is called “nonlocality without entanglement” since each local party has a limit to full information of given quantum states. When this property is extended to the case of minimum-error discrimination, one can see that it is revealed when a nonlocal measurement provides more information about the unentangled states than LOCC does. One may infer the fact that the property depends on quantum states composing the quantum ensemble. However, an essential but unsettled question about the property is whether an explicit dependence on prior probabilities in terms of minimum-error discrimination could be shown in nonlocality without entanglement. In a simple term, one can ask whether different quantum ensembles made of the same separable quantum states could exhibit explicitly different behavior of the nonlocality. We answer this question in the positive, and we furthermore provide the explicit functional dependence of guessing probability on prior probabilities for the mirror-symmetric ensemble.
A low cost, short range quantum key distribution system

Abstract We present a miniaturized quantum key distribution system, designed to augment the more mature quantum key distribution systems currently commercially available. Our device is designed for the consumer market, and so size, weight and power are more important than raw performance. To achieve our form factor, the transmitter is handheld and the receiver is a larger fixed terminal. We envisage users would bring their transmitters to centrally located receivers and exchange keys which they could use at a later point. Transmitting qubits at 80 MHz, the peak key rate is in excess of 20 kbps. The transmitter device fits within an envelope of &lt;150 ml, weighs 65 g and consumes 3.15 W of power.
Do foreign aid triggers economic growth in Chad? A time series analysis

Abstract This study explores the nexus between foreign aid and Chad's economic growth. Empirical evidence is based on annual data from 1982 to 2018. The study used ARDL, FMOLS, and DOLS techniques to establish interconnection among the economic indicators. Subsequently, the study utilized the wavelet coherence technique to capture causality and correlation between economic growth and the independent variables. One of the wavelet approach's uniqueness is that it shows the pattern and behavior of the variables used, including the different time horizons. Thus, we explore the dynamic influence of gross capital formation, foreign aid, import, and export on Chad's economic growth. The result of the ARDL long-run estimates reveals that gross capital formation and foreign aid exert insignificant impact on GDP growth. However, exports and imports exert a positive and significant impact on GDP growth. Furthermore, the global financial crisis has a negative and significant impact on the economy of Chad. The outcomes of the wavelet coherence test provide supportive evidence for the ARDL long-run outcomes. Hence, we suggested that substantial macroeconomic reforms and economic liberalization initiatives will help in the dissemination of information and promote domestic investment and importation of high-tech goods.
Don't Trust Your Eyes: Image Manipulation in the Age of DeepFakes

We review the phenomenon of deepfakes, a novel technology enabling inexpensive manipulation of video material through the use of artificial intelligence, in the context of today’s wider discussion on fake news. We discuss the foundation as well as recent developments of the technology, as well as the differences from earlier manipulation techniques and investigate technical countermeasures. While the threat of deepfake videos with substantial political impact has been widely discussed in recent years, so far, the political impact of the technology has been limited. We investigate reasons for this and extrapolate the types of deepfake videos we are likely to see in the future.
A hierarchy of expert performance (HEP) applied to digital forensics: Reliability and biasability in digital forensics decision making

In order to examine the biasability (impact of contextual information) and reliability (consistency) of digital forensic observations, interpretations, and conclusions, 53 digital forensics (DF) examiners analysed the same evidence file. For biasability, some DF examiners were provided with contextual information suggesting guilt or innocence, while a control group received no contextual information. As per biasability, the results showed that the DF examiners' observations were affected by the biasing contextual information. As per reliability, the results showed low reliability between DF examiners in observations, interpretations, and conclusions. For improving DF work, as well as for transparency, it is important to study and assess the biasability and reliability of their decision making.
Combating Challenges in the Construction Industry with Blockchain Technology

Blockchain technology serves a breakthrough for many solutions like that of poor payment facilities, lesser productivity, trust among different levels of the industry in sharing of the gathered field reports, or the commands of the higher authority in the construction industry. Thus, the decentralized ledger system has facilitated an automatic chain of the flow of data or information among the peers and the subordinates. The aim is to make a digital world more trustable giving a new open arena to a better security solution, resilient nature and the enhancement of the systems. It is already known that industry progress upon its embracing of newer technologies that bind a better relationship with the customers and the giving of the best quality project basing on the low cost of trade estimates; the blockchain facilitates the use of smart contracts, agreements with the party directly without the involvement of a third party. The new version of the industries is supposedly aiming towards more digitized industrialization with more involvement in construction management with building information modelling (BIM). The aim is to provide a pre-step towards the use cases of blockchain technology in the construction industry after the reviving of the industry using the utmost digitization.
Optimal Scale of Hierarchical Image Segmentation with Scribbles Guidance for Weakly Supervised Semantic Segmentation

Deep convolutional neural networks (DCNNs) trained on the pixel-level annotated images have achieved improvements in semantic segmentation. Due to the high cost of labeling training data, their applications may have great limitation. However, weakly supervised segmentation approaches can significantly reduce human labeling efforts. In this paper, we introduce a new framework to generate high-quality initial pixel-level annotations. By using a hierarchical image segmentation algorithm to predict the boundary map, we select the optimal scale of high-quality hierarchies. In the initialization step, scribble annotations and the saliency map are combined to construct a graphic model over the optimal scale segmentation. By solving the minimal cut problem, it can spread information from scribbles to unmarked regions. In the training process, the segmentation network is trained by using the initial pixel-level annotations. To iteratively optimize the segmentation, we use a graphical model to refine segmentation masks and retrain the segmentation network to get more precise pixel-level annotations. The experimental results on Pascal VOC 2012 dataset demonstrate that the proposed framework outperforms most of weakly supervised semantic segmentation methods and achieves the state-of-the-art performance, which is [Formula: see text] mIoU.
Visual–Tactile Fused Graph Learning for Object Clustering

Object clustering has received considerable research attention most recently. However, 1) most existing object clustering methods utilize visual information while ignoring important tactile modality, which would inevitably lead to model performance degradation and 2) simply concatenating visual and tactile information via multiview clustering method can make complementary information to not be fully explored, since there are many differences between vision and touch. To address these issues, we put forward a graph-based visual-tactile fused object clustering framework with two modules: 1) a modality-specific representation learning module MR and 2) a unified affinity graph learning module MU . Specifically, MR focuses on learning modality-specific representations for visual-tactile data, where deep non-negative matrix factorization (NMF) is adopted to extract the hidden information behind each modality. Meanwhile, we employ an autoencoder-like structure to enhance the robustness of the learned representations, and two graphs to improve its compactness. Furthermore, MU highlights how to mitigate the differences between vision and touch, and further maximize the mutual information, which adopts a minimizing disagreement scheme to guide the modality-specific representations toward a unified affinity graph. To achieve ideal clustering performance, a Laplacian rank constraint is imposed to regularize the learned graph with ideal connected components, where noises that caused wrong connections are removed and clustering labels can be obtained directly. Finally, we propose an efficient alternating iterative minimization updating strategy, followed by a theoretical proof to prove framework convergence. Comprehensive experiments on five public datasets demonstrate the superiority of the proposed framework.
An Efficient Sharing Grouped Convolution via Bayesian Learning

Compared with traditional convolutions, grouped convolutional neural networks are promising for both model performance and network parameters. However, existing models with the grouped convolution still have parameter redundancy. In this article, concerning the grouped convolution, we propose a sharing grouped convolution structure to reduce parameters. To efficiently eliminate parameter redundancy and improve model performance, we propose a Bayesian sharing framework to transfer the vanilla grouped convolution to be the sharing structure. Intragroup correlation and intergroup importance are introduced into the prior of the parameters. We handle the Maximum Type II likelihood estimation problem of the intragroup correlation and intergroup importance by a group LASSO-type algorithm. The prior mean of the sharing kernels is iteratively updated. Extensive experiments are conducted to demonstrate that on different grouped convolutional neural networks, the proposed sharing grouped convolution structure with the Bayesian sharing framework can reduce parameters and improve prediction accuracy. The proposed sharing framework can reduce parameters up to 64.17%. For ResNeXt-50 with the sharing grouped convolution on ImageNet dataset, network parameters can be reduced by 96.875% in all grouped convolutional layers, and accuracies are improved to 78.86% and 94.54% for top-1 and top-5, respectively.