Reviewerly/exHarmony huggingface dataset:
Benchmarking Scalable Predictive Uncertainty in Text Classification

This paper explores the question of how predictive uncertainty methods perform in practice in Natural Language Processing, specifically multi-class and multi-label text classification. We conduct benchmarking experiments with 1-D convolutional neural networks and pre-trained transformers on six real-world text classification datasets in which we empirically investigate why popular scalable uncertainty estimation strategies ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Monte-Carlo Dropout</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Ensemble</i> ) and notable extensions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heteroscedastic</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Concrete Dropout</i> ) underestimate uncertainty. We motivate that uncertainty estimation benefits from combining posterior approximation procedures, linking it to recent research on how ensembles and variational Bayesian methods navigate the loss landscape. We find that our proposed method combination of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Ensemble</i> with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Concrete Dropout</i> , by analysis of in- domain calibration, cross-domain classification, and novel class robustness, demonstrates superior performance, even at a smaller ensemble size. Our results corroborate the importance of fine-tuning dropout rate to the text classification task at hand, which individually and as an ensemble impacts model robustness. We observe in ablation that pre-trained transformers severely underperform in novelty detection, limiting the applicability of transfer learning when distribution shift from novel classes can be expected.
H-EMD: A Hierarchical Earth Mover’s Distance Method for Instance Segmentation

Deep learning (DL) based semantic segmentation methods have achieved excellent performance in biomedical image segmentation, producing high quality probability maps to allow extraction of rich instance information to facilitate good instance segmentation. While numerous efforts were put into developing new DL semantic segmentation models, less attention was paid to a key issue of how to effectively explore their probability maps to attain the best possible instance segmentation. We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances. Further, the generated instance candidates form a well-behaved hierarchical structure (a forest), which allows selecting instances in an optimized manner. Hence, we propose a novel framework, called hierarchical earth mover's distance (H-EMD), for instance segmentation in biomedical 2D+time videos and 3D images, which judiciously incorporates consistent instance selection with semantic-segmentation-generated probability maps. H-EMD contains two main stages: (1) instance candidate generation: capturing instance-structured information in probability maps by generating many instance candidates in a forest structure; (2) instance candidate selection: selecting instances from the candidate set for final instance segmentation. We formulate a key instance selection problem on the instance candidate forest as an optimization problem based on the earth mover's distance (EMD), and solve it by integer linear programming. Extensive experiments on eight biomedical video or 3D datasets demonstrate that H-EMD consistently boosts DL semantic segmentation models and is highly competitive with state-of-the-art methods.
Hybrid deep-learning framework for object-based forgery detection in video

Detection of object-based video forgery is receiving attention in recent years. However, until recently, dominant object-based forgery detectors are still based on hand-crafted features and their performances are not satisfactory. In this paper, we propose a novel hybrid deep-learning network which for the first time incorporates two-dimensional/three-dimensional convolutional neural network and recurrent neural network for object-based video forgery detection in videos with advanced encoding formats. Please note that the proposed framework is a full end-to-end data-driven solution. It is comprised of a specifically initialized three-dimensional convolutional layer which tries to mix up primitive intra-frame and inter-frame features, a two-dimensional CNN which tries to extract low-dimensional intra-frame features, and a four-layer bi-directional LSTM network which tries to catch high-level temporal features. Using this way, our proposed approach catches the intra-frame and inter-frame inherent properties of a target video with a united framework. The extensive experiments conducted on the largest object-based forged video database ever reported in the literature show that our hybrid framework achieves superior performance in forged video detection and forged segment localization. Moreover, the experiments conducted on datasets of videos with degraded quality demonstrated that our proposed framework is more robust in real-life scenarios.
Semantic-aware Speech to Text Transmission with Redundancy Removal

Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wireless communication methods that focus on the transmission of abstract symbols, semantic communication approaches attempt to achieve better transmission efficiency by only sending the semantic-related information of the source data. In this paper, we consider semantic-oriented speech to text transmission. We propose a novel end-to-end DL-based transceiver, which includes an attention-based soft alignment module and a redundancy removal module to compress the transmitted data. In particular, the former extracts only the text-related semantic features, and the latter further drops the semantically redundant content, greatly reducing the amount of semantic redundancy compared to existing methods. We also propose a two-stage training <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sc</sup> heme, which speeds up the training of the proposed DL model. The simulation results indicate that our proposed method outperforms current methods in terms of the accuracy of the received text and transmission efficiency. Moreover, the proposed method also has a smaller model size and shorter end-to-end runtime.
Blockchain-Based Framework for Secure Medical Information in Internet of Things System

Blockchain technology has been proved useful in modern healthcare systems by increasing the security and privacy of the users. The complexity and costly characteristics of healthcare system can be reduced through the application of blockchain and improvement on medical record management. This has been proved useful in transactions involving medical records, security of data, smart contacts, and insurance billing and by providing a distributed database of transactions. On the authentication of the users associated with the healthcare records, the blockchain network likewise adheres to the ideal of maximum confidentiality, thus help in securing the medical information. Therefore, this chapter explains the important role place by blockchain in securing medicine data, the applicability, and challenges of the techniques. Also, it proposes an architecture based on blockchain to secure medical data in Internet of Things-based healthcare system. The concern of security and privacy in healthcare system can be reduced if the blockchain technology is used in healthcare system. Monitoring, regulating, and sensing in accordance with the blockchain as a transaction and access management system for laying out accurate and trust data for the advantages of patients across and a suitable medium for medical treatment can alleviate the problems of correct data mitigation.
Hamiltonian Simulation with Random Inputs

The algorithmic error of digital quantum simulations is usually explored in terms of the spectral norm distance between the actual and ideal evolution operators. In practice, this worst-case error analysis may be unnecessarily pessimistic. To address this, we develop a theory of average-case performance of Hamiltonian simulation with random initial states. We relate the average-case error to the Frobenius norm of the multiplicative error and give upper bounds for the product formula (PF) and truncated Taylor series methods. As applications, we estimate average-case error for the digital Hamiltonian simulation of general lattice Hamiltonians and k-local Hamiltonians. In particular, for the nearest-neighbor Heisenberg chain with n spins, the error is quadratically reduced from O(n) in the worst case to O(sqrt[n]) on average for both the PF method and the Taylor series method. Numerical evidence suggests that this theory accurately characterizes the average error for concrete models. We also apply our results to error analysis in the simulation of quantum scrambling.
GPU-based Monte Carlo ray tracing simulation considering refraction for central receiver system

The heliostat in the central receiver system usually adopts silvered-glass reflectors, where a glass layer covers a specular reflection layer. Previous flux density distribution simulation methods ignored the refraction effects caused by the glass layer. This paper proposes a more accurate Monte Carlo ray-tracing simulation method considering refraction and total internal reflection (TIR) effects caused by the ray transmission in the glass layer. The proposed simulation method is fully designed and implemented on a graphics processing unit (GPU), which enables the algorithm performance to remain effective even when the simulation is more consistent with the real situation. Experiments and simulations reveal that refraction has non-negligible effects on the simulation results. Compared with the classical Monte Carlo ray-tracing simulation method that only considers the ray's reflection, refraction reduces the maximum radiative flux and total energy by up to 80% and 50%, respectively. Refraction also causes the flux spot reflected on the receiver panel to spread greatly. In several extreme cases, the ray is trapped in the glass due to TIR.
Lattice-Based Searchable Encryption Scheme Against Inside Keywords Guessing Attack

To save the local storage, users store the data on the cloud server who offers convenient internet services. To guarantee the data privacy, users encrypt the data before uploading them into the cloud server. Since encrypti... | Find, read and cite all the research you need on Tech Science Press
Predicting Most Productive Requirements Elicitation Teams using MBTI Personality Traits Model

The social and collaborative nature of requirements elicitation process bases its core dependency on aptitude, attitudes, and personality characteristics of its participants. The participant’s personality characteristics are directly related with their personality traits, which can be categorized using different personality assessment models. The MBTI personality assessment model has been used successfully for the assessment of personality of software engineers since last few decades. In this article, the personality traits for requirements elicitation teams have been predicted using MBTI personality assessment model, on the basis of their industry demanded job descriptions/tasks and major soft skills. The article presents a complete personality prediction process using a systematic approach based on major soft skills mapping with job descriptions, personality attributes and personality traits. The obtained results show that extraversion and feeling personality traits are the most suitable personality traits for requirements analysts/engineers who are assigned the task of requirements elicitation. The obtained results are very much aligned with the already published scholar’s work for software engineer’s personality assessment and development team composition.
Scrutinizing entropic uncertainty and quantum discord in an open system under quantum critical environment

Abstract We explore the dynamics of quantum discord (QD) and entropic uncertainty lower bound for a two-qubit system independently coupled to an Ising spin chain. For this model, we obtain the analytic results of QD and uncertainty bound and find that there is a trade-off between them. We also examine the effect of the number of spins and the coupling strength to a transverse field on these two quantum criteria.
SMS OTP Security (SOS)

SMS-based two-factor authentication (2FA) is the most widely deployed 2FA mechanism, despite the fact that SMS messages are known to be vulnerable to rerouting attacks, and despite the availability of alternatives that may be more secure. This is for two reasons. First, it is very effective in practice, as evidenced by reports from Google and Microsoft. Second, users prefer SMS over alternatives, because text messaging is already part of their daily communication. Accepting this practical reality, we developed a new SMS-based protocol that makes rerouting attacks useless to adversaries who aim to take over user accounts. Our protocol delivers one-time passwords (OTP) via text message in a manner that adds minimal overhead (to both the user and the server) over existing SMS-based methods, and is implemented with only small changes to the stock text-message applications that already ship on mobile phones. The security of our protocol rests upon a provably secure authenticated key exchange protocol that, crucially, does not place significant new burdens upon the user. Indeed, we carry out a user study that demonstrates no statistically significant difference between traditional SMS and our protocol, in terms of usability.
What Is Coming across the Horizon and How Can We Handle It? Bitcoin Scenarios as a Starting Point for Rigorous and Relevant Research

The disruptive impact of blockchain technologies can be felt across numerous industries as it threatens to disrupt existing business models and economic structures. To better understand this impact, academic researchers regularly apply well-established theories and methods. The vast majority of these approaches are based on multivariate methods that rely on average behavior and treat extreme cases as outliers. However, as recent history has shown, current developments in blockchain and cryptocurrencies are frequently characterized by aberrant behavior and unexpected events that shape individuals’ perceptions, market behavior, and public policymaking. In this paper, I apply various scenario tools to identify such extreme scenarios and illustrate their underlying structure as bundles of interdependent factors. Using the case of Bitcoin, I illustrate that the identification of extreme positive and negative scenarios is complex and heavily depends on underlying economic assumptions. I present three scenarios in which Bitcoin is characterized as a financial savior, as a severe threat to economic stability, or as a substitute to overcome several shortcomings of the existing financial system. The research questions that can be derived from these scenarios bridge behavioral and design science research and provide a fertile ground for impactful future research.
Semi-Automatic Prostate Segmentation From Ultrasound Images Using Machine Learning and Principal Curve Based on Interpretable Mathematical Model Expression

Accurate prostate segmentation in transrectal ultrasound (TRUS) is a challenging problem due to the low contrast of TRUS images and the presence of imaging artifacts such as speckle and shadow regions. To address this issue, we propose a semi-automatic model termed Hybrid Segmentation Model (H-SegMod) for prostate Region of Interest (ROI) segmentation in TRUS images. H-SegMod contains two cascaded stages. The first stage is to obtain the vertices sequences based on an improved principal curve-based model, where a few radiologist-selected seed points are used as prior. The second stage is to find a map function for describing the smooth prostate contour based on an improved machine learning model. Experimental results show that our proposed model achieved superior segmentation results compared with several other state-of-the-art models, achieving an average Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (Ω), and Accuracy (ACC) of 96.5%, 95.2%, and 96.3%, respectively.
Validation of whole slide imaging for frozen section diagnosis of lymph node metastasis: A retrospective study from a tertiary care hospital in Thailand

The use of whole slide imaging (WSI) for frozen section (FS) diagnosis is helpful, particularly in the context of pathologist shortages. However, there is minimal data on such usage in resource-limited settings. This study aims to validate the use of WSI for FS diagnosis of lymph node metastasis using a low-cost virtual microscope scanner with consumer-grade laptops at a tertiary care hospital in Thailand.FS slides were retrieved for which the clinical query was to evaluate lymph node metastasis. They were digitized by a virtual microscope scanner (MoticEasyScan, Hong Kong) using up to 40× optical magnification. Three observers with different pathology experience levels diagnosed each slide, reviewing glass slides (GS) followed by digital slides (DS) after two weeks of a wash out period. WSI and GS diagnoses were compared. The time used for scanning and diagnosis of each slide was recorded.295 FS slides were retrieved and digitized. The first-time successful scanning rate was 93.6 %. The mean scanning time was 2 min per slide. Both intraobserver agreement and interobserver agreement of WSI and GS diagnoses were high (Cohen's K; kappa value >0.84). The time used for DS diagnosis decreased as the observer's experience with WSI increased.Despite varying pathological experiences, observers using WSI provided accurate FS diagnoses of lymph node metastasis. The time required for DS diagnoses decreased with additional observer's experience with WSI. Therefore, a WSI system containing low-cost scanners and consumer-grade laptops could be used for FS services in hospital laboratories lacking pathologists.
Unsupervised Black-Box Model Domain Adaptation for Brain Tumor Segmentation

Unsupervised domain adaptation (UDA) is an emerging technique that enables the transfer of domain knowledge learned from a labeled source domain to unlabeled target domains, providing a way of coping with the difficulty of labeling in new domains. The majority of prior work has relied on both source and target domain data for adaptation. However, because of privacy concerns about potential leaks in sensitive information contained in patient data, it is often challenging to share the data and labels in the source domain and trained model parameters in cross-center collaborations. To address this issue, we propose a practical framework for UDA with a black-box segmentation model trained in the source domain only, without relying on source data or a white-box source model in which the network parameters are accessible. In particular, we propose a knowledge distillation scheme to gradually learn target-specific representations. Additionally, we regularize the confidence of the labels in the target domain via unsupervised entropy minimization, leading to performance gain over UDA without entropy minimization. We extensively validated our framework on a few datasets and deep learning backbones, demonstrating the potential for our framework to be applied in challenging yet realistic clinical settings.
Image Retrieval for Local Architectural Heritage Recommendation Based on Deep Hashing

Propagating architectural heritage is of great significance to the inheritance and protection of local culture. Recommendations based on user preferences can greatly benefit the promotion of local architectural heritage so as to better protect and inherit historical culture. Thus, a powerful tool is necessary to build such a recommendation system. Recently, deep learning methods have proliferated as a means to analyze data in architectural domains. In this paper, based on a case study of Jiangxi, China, we explore a recommendation system for the architectural heritage of a local area. To organize our experiments, a dataset for traditional Chinese architecture heritage is constructed and a deep hashing retrieval method is proposed for the recommendation task. By utilizing a data fine-tuning strategy, our retrieval method can realize high-accuracy recommendation and break the model training restriction caused by insufficient data on local architectural heritage. Furthermore, we analyze the retrieval answers and map the data into a two-dimensional space to reveal the relationships between different architectural heritage categories. An image-to-location application is also provided for a better user experience.
Inference-Based Quantum Sensing

In a standard quantum sensing (QS) task one aims at estimating an unknown parameter θ, encoded into an n-qubit probe state, via measurements of the system. The success of this task hinges on the ability to correlate changes in the parameter to changes in the system response R(θ) (i.e., changes in the measurement outcomes). For simple cases the form of R(θ) is known, but the same cannot be said for realistic scenarios, as no general closed-form expression exists. In this Letter, we present an inference-based scheme for QS. We show that, for a general class of unitary families of encoding, R(θ) can be fully characterized by only measuring the system response at 2n+1 parameters. This allows us to infer the value of an unknown parameter given the measured response, as well as to determine the sensitivity of the scheme, which characterizes its overall performance. We show that inference error is, with high probability, smaller than δ, if one measures the system response with a number of shots that scales only as Ω(log^{3}(n)/δ^{2}). Furthermore, the framework presented can be broadly applied as it remains valid for arbitrary probe states and measurement schemes, and, even holds in the presence of quantum noise. We also discuss how to extend our results beyond unitary families. Finally, to showcase our method we implement it for a QS task on real quantum hardware, and in numerical simulations.