Reviewerly/exHarmony huggingface dataset:
Probability-Based Graph Embedding Cross-Domain and Class Discriminative Feature Learning for Domain Adaptation

Feature-based domain adaptation methods project samples from different domains into the same feature space and try to align the distribution of two domains to learn an effective transferable model. The vital problem is how to find a proper way to reduce the domain shift and improve the discriminability of features. To address the above issues, we propose a unified Probability-based Graph embedding Cross-domain and class Discriminative feature learning framework for unsupervised domain adaptation (PGCD). Specifically, we propose novel graph embedding structures to be the class discriminative transfer feature learning item and cross-domain alignment item, which can make the same-category samples compact in each domain, and fully align the local and global geometric structure across domains. Besides, two theoretical analyses are given to prove the interpretability of the proposed graph structures, which can further describe the relationships between samples to samples in single-domain and cross-domain transfer feature learning scenarios. Moreover, we adopt novel weight strategies via probability information to generate robust centroids in each proposed item to enhance the accuracy of transfer feature learning and reduce the error accumulation. Compared with the advanced approaches by comprehensive experiments, the promising performance on the benchmark datasets verify the effectiveness of the proposed model.
Safety Verification of Neural Feedback Systems Based on Constrained Zonotopes

Artificial neural networks have recently been utilized in many feedback control systems and introduced new challenges regarding the safety of such systems. This paper considers the safe verification problem for a dynamical system with a given feedforward neural network as the feedback controller by using a constrained zonotope-based approach. A novel set-based method is proposed to compute both exact and over-approximated reachable sets for neural feedback systems with linear models, and linear program-based sufficient conditions are presented to verify whether the trajectories of such a system can avoid unsafe regions represented as constrained zonotopes. The results are also extended to neural feedback systems with nonlinear models. The computational efficiency and accuracy of the proposed method are demonstrated by two numerical examples where a comparison with state-of-the-art methods is also provided.
Improved Algorithms of Quantum Imaginary Time Evolution for Ground and Excited States of Molecular Systems

Quantum imaginary time evolution (QITE) is a recently proposed quantum-classical hybrid algorithm that is guaranteed to reach the lowest state of systems. In this study, we present several improvements on QITE, mainly focusing on molecular applications. We analyze the derivation of the underlying QITE equation order-by-order and suggest a modification that is theoretically well founded. Our results clearly indicate the soundness of the here-derived equation, enabling a better approximation of the imaginary time propagation by a unitary. We also discuss how to accurately estimate the norm of an imaginary-time-evolved state and applied it to excited-state calculations using the quantum Lanczos algorithm. Finally, we propose the folded-spectrum QITE scheme as a straightforward extension of QITE for general excited-state simulations. The effectiveness of all these developments is illustrated by simulations with or without noise effect, offering further insights into quantum algorithms for imaginary time evolution.
Position-Controlled Telecom Single Photon Emitters Operating at Elevated Temperatures

A key resource in quantum-secured communication protocols are single photon emitters. For long-haul optical networks, it is imperative to use photons at wavelengths compatible with telecom single mode fibers. We demonstrate high purity single photon emission at 1.31 μm using deterministically positioned InP photonic waveguide nanowires containing single InAsP quantum dot-in-a-rod structures. At excitation rates that saturate the emission, we obtain a single photon collection efficiency at first lens of 27.6% and a probability of multiphoton emission of g(2)(0) = 0.021. We have also evaluated the performance of the source as a function of temperature. Multiphoton emission probability increases with temperature with values of 0.11, 0.34, and 0.57 at 77, 220 and 300 K, respectively, which is attributed to an overlap of temperature-broadened excitonic emission lines. These results are a promising step toward scalably fabricating telecom single photon emitters that operate under relaxed cooling requirements.
A critical reflection on analytics and artificial intelligence based analytics in hospitality and tourism management research

Purpose This work consists of a critical reflection on the extent to which hospitality and tourism management scholars have accurately used the term “analytics” and its five types (i.e. descriptive, exploratory, predictive, prescriptive and cognitive analytics) in their research. Only cognitive analytics, the latest and most advanced type, is based on artificial intelligence (AI) and requires machine learning (ML). As cognitive analytics constitutes the cutting edge in industry application, this study aims to examine in depth the extent cognitive analytics has been covered in the literature. Design/methodology/approach This study is based on a systematic literature review (SLR) of the hospitality and tourism literature on the topic of “analytics”. The SLR findings were complemented by the results of an additional search query based on “machine learning” and “deep learning” that was used as a robustness check. Moreover, the SLR findings were triangulated with recent literature reviews on related topics (e.g. big data and AI) to generate additional insights. Findings The findings of this study show that: there is a growing and accelerating body of research on analytics; the literature lacks a consistent use of terminology and definitions related to analytics. Specifically, publications rarely use scientific definitions of analytics and their different types; although AI and ML are key enabling technologies for cognitive analytics, hospitality and tourism management research did not explicitly link these terms to analytics and did not distinguish cognitive analytics from other forms of analytics that do not rely on ML. In fact, the term “cognitive analytics” is apparently missing in the hospitality and tourism management literature. Research limitations/implications This study generates a set of eight theoretical and three practical implications and advance theoretical and methodological recommendations for further research. Originality/value To the best of the authors’ knowledge, this is the first study that explicitly and critically examines the use of analytics in general, and cognitive analytics in particular, in the hospitality and tourism management literature.
Performance Analysis of Deep Learning Algorithms in Diagnosis of Malaria Disease

Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease’s impact on the population, time series prediction models are necessary. The conventional technique of detecting malaria disease is for certified technicians to examine blood smears visually for parasite-infected RBC (red blood cells) underneath a microscope. This procedure is ineffective, and the diagnosis depends on the individual performing the test and his/her experience. Automatic image identification systems based on machine learning have previously been used to diagnose malaria blood smears. However, so far, the practical performance has been insufficient. In this paper, we have made a performance analysis of deep learning algorithms in the diagnosis of malaria disease. We have used Neural Network models like CNN, MobileNetV2, and ResNet50 to perform this analysis. The dataset was extracted from the National Institutes of Health (NIH) website and consisted of 27,558 photos, including 13,780 parasitized cell images and 13,778 uninfected cell images. In conclusion, the MobileNetV2 model outperformed by achieving an accuracy rate of 97.06% for better disease detection. Also, other metrics like training and testing loss, precision, recall, fi-score, and ROC curve were calculated to validate the considered models.
Scalable Lightweight Protocol for Interoperable Public Blockchain-Based Supply Chain Ownership Management

Scalability prevents public blockchains from being widely adopted for Internet of Things (IoT) applications such as supply chain management. Several existing solutions focus on increasing the transaction count, but none of them address scalability challenges introduced by resource-constrained IoT device integration with these blockchains, especially for the purpose of supply chain ownership management. Thus, this paper solves the issue by proposing a scalable public blockchain-based protocol for the interoperable ownership transfer of tagged goods, suitable for use with resource-constrained IoT devices such as widely used Radio Frequency Identification (RFID) tags. The use of a public blockchain is crucial for the proposed solution as it is essential to enable transparent ownership data transfer, guarantee data integrity, and provide on-chain data required for the protocol. A decentralized web application developed using the Ethereum blockchain and an InterPlanetary File System is used to prove the validity of the proposed lightweight protocol. A detailed security analysis is conducted to verify that the proposed lightweight protocol is secure from key disclosure, replay, man-in-the-middle, de-synchronization, and tracking attacks. The proposed scalable protocol is proven to support secure data transfer among resource-constrained RFID tags while being cost-effective at the same time.
nestedcv: an R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high-dimensional data

Abstract Motivation Although machine learning models are commonly used in medical research, many analyses implement a simple partition into training data and hold-out test data, with cross-validation (CV) for tuning of model hyperparameters. Nested CV with embedded feature selection is especially suited to biomedical data where the sample size is frequently limited, but the number of predictors may be significantly larger (P ≫ n). Results The nestedcv R package implements fully nested k × l-fold CV for lasso and elastic-net regularized linear models via the glmnet package and supports a large array of other machine learning models via the caret framework. Inner CV is used to tune models and outer CV is used to determine model performance without bias. Fast filter functions for feature selection are provided and the package ensures that filters are nested within the outer CV loop to avoid information leakage from performance test sets. Measurement of performance by outer CV is also used to implement Bayesian linear and logistic regression models using the horseshoe prior over parameters to encourage a sparse model and determine unbiased model accuracy. Availability and implementation The R package nestedcv is available from CRAN: https://CRAN.R-project.org/package=nestedcv.
Pattern recognition in reciprocal space with a magnon-scattering reservoir

Magnons are elementary excitations in magnetic materials and undergo nonlinear multimode scattering processes at large input powers. In experiments and simulations, we show that the interaction between magnon modes of a confined magnetic vortex can be harnessed for pattern recognition. We study the magnetic response to signals comprising sine wave pulses with frequencies corresponding to radial mode excitations. Three-magnon scattering results in the excitation of different azimuthal modes, whose amplitudes depend strongly on the input sequences. We show that recognition rates above 95\% can be attained for four-symbol sequences using the scattered modes, with strong performance maintained with the presence of amplitude noise in the inputs.
A Detailed Study on the 8 Queens Problem Based on Algorithmic Approaches Implemented in PASCAL Programming Language

The eight queens problem was a topic for concern of many great mathematicians such as Gauss, Nauck, Guenther and Glaisher among others. It is a problem that explores how to set eight chess queens on an 8 × 8 chessboard by having in mind to implement the constraint that no two queens can attack one another. Therefore, a solution can be achieved in that problem in case that no two queens possess at the same time the same column, row, or diagonal. The eight queens problem was first posed in the mid-19th century and nowadays, it is used in many cases for numerous computer programming techniques such as secure data hiding. This study tries to contribute to a better understanding of the 8-Queens problem by describing and implementing various algorithmic approaches implemented in PASCAL Programming Language, with main purpose to find optimal solutions to the problem. In the last section, a useful discussion is conducted for further investigation of the A* algorithm to offer better solution to Queens problem in different dimensions (n × n).
Outlier Detection Using Three-Way Neighborhood Characteristic Regions and Corresponding Fusion Measurement

Outliers carry significant information to reflect an anomaly mechanism, so outlier detection facilitates relevant data mining. In terms of outlier detection, the classical approaches from distances apply to numerical data rather than nominal data, while the recent methods on basic rough sets deal with nominal data rather than numerical data. Aiming at wide outlier detection on numerical, nominal, and hybrid data, this paper investigates three-way neighborhood characteristic regions and corresponding fusion measurement to advance outlier detection. First, neighborhood rough sets are deepened via three-way decision, so they derive three-way neighborhood structures on model boundaries, inner regions, and characteristic regions. Second, the three-way neighborhood characteristic regions motivate the information fusion and weight measurement regarding all features, and thus, a multiple neighborhood outlier factor emerges to establish a new method of outlier detection; furthermore, a relevant outlier detection algorithm (called 3WNCROD) is designed to comprehensively process numerical, nominal, and mixed data. Finally, the 3WNCROD algorithm is experimentally validated, and it generally outperforms 13 contrast algorithms to perform better for outlier detection.
Chaos-based block permutation and dynamic sequence multiplexing for video encryption

Abstract This paper proposes a video security transmission enhancement algorithm based on block permutation and dynamic multiplexing sequences encryption based on 4D autonomous hyperchaotic system. Firstly, we employ the block permutation encryption and diffusion confusion encryption module, which is based on dynamic multiplexing chaotic sequences, to encrypt the plaintext and obtain the ciphertext. Subsequently, the hash value of this round’s ciphertext is utilized to generate the chaotic key, produced by the multiplexing sequence of this round after mathematical processing. Then, the key is used to generate the chaotic sequence to confuse the N-th of the multiplexed sequence, and the next round of multiplexed sequence is obtained. If the current round of chaotic sequence has been completely confused, the chaotic sequence is re-generated to generate a new multiplex sequence by using the key generated by the current round key and the initial key. Finally, the above steps are repeated for the encryption of each frame of the video. Compared with the traditional permutation coding algorithm, it increases the difficulty of estimation or recognition while ensuring efficiency, and effectively improves the avalanche effect of the algorithm. Through frame by frame ciphertext closed-loop feedback, it has the ability to resist known plaintext attack and selected plaintext attack. The results show that the scheme has high security and significant diffusion characteristics, and can effectively resist various common cryptographic attacks.
The True Role of Active Communicators: An Empirical Study of Jazz Core  Developers

Context: Interest in software engineering (SE) methodologies and tools has been complemented in recent years by research efforts oriented towards understanding the human processes involved in software development. This shift has been imperative given reports of inadequately performing teams and the consequent growing emphasis on individuals and team relations in contemporary SE methods. Objective: While software repositories have frequently been studied with a view to explaining such human processes, research has tended to use primarily quantitative analysis approaches. There is concern, however, that such approaches can provide only a partial picture of the software process. Given the way human behavior is nuanced within psychological and social contexts, it has been asserted that a full understanding may only be achieved through deeper contextual enquiries. Method: We have followed such an approach and have applied data mining, SNA, psycholinguistic analysis and directed content analysis (CA) to study the way core developers at IBM Rational Jazz contribute their social and intellectual capital, and have compared the attitudes, interactions and activities of these members to those of their less active counterparts. Results: Among our results, we uncovered that Jazz's core developers worked across multiple roles, and were crucial to their teams' organizational, intra-personal and inter-personal processes. Additionally, although these individuals were highly task- and achievement-focused, they were also largely responsible for maintaining positive team atmosphere, and for providing context awareness in support of their colleagues. Conclusion: Our results suggest that high-performing distributed agile teams rely on both individual and collective efforts, as well as organizational environments that promote informal and organic work structures.(Abridged)
A security requirements modelling language for cloud computing environments

This paper presents a novel security modelling language and a set of original analysis techniques, for capturing and analysing security requirements for cloud computing environments. The novelty of the language lies in the integration of concepts from cloud computing, with concepts from security and goal-oriented requirements engineering to elicit, model and analyse security requirements for cloud infrastructures. We then propose three analysis techniques, which support an automated process where given a model of a cloud computing system, developed with the proposed language, will enhance the model with new security knowledge, for example threats and vulnerabilities, mitigation strategies and assets and actor responsibilities. This is, to the best of our knowledge, the first attempt in the literature to develop a language for cloud computing security modelling and analysis, based on such integration, and support it with a set of automated techniques that enhanced the stakeholder-created models with security knowledge. The proposed modelling language and techniques are illustrated through walking examples and a case study based on our work in the VisiOn European project.
Complementary realities: Public domain Internet measurements in the development of Canada’s universal access policies

As access to the Internet has become increasingly essential for social and economic participation, public domain Internet measurements have become indispensable for users to validate quality of service their network operator delivers and for policymakers to identify and address gaps in broadband infrastructure. This article evaluates public domain Internet performance measurements available for assessing the state of connectivity and developing universal access service quality standards in Canada. The analysis suggests that different approaches to Internet measurement represent complementary windows into a complex and fast evolving reality of broadband connectivity. Despite their potential shortcomings, large-scale crowdsourced open data network testing platforms have a central role to play in enabling broadband infrastructure policy coordination across different levels of government, empowerment of consumers, and achievement of universal service objectives for quality of service users experience when accessing the open Internet.
Modeling Latent Relation to Boost Things Categorization Service

While it is well understood that the Internet of things (IoT) offers the capability of integrating the physical world and the cyber world, it also presents many significant challenges with numerous heterogeneous things connected and interacted, such as how to efficiently annotate things with semantic labels (i.e., things categorization) for searching and recommendation. Traditional ways for things categorization are not effective due to several characteristics (e.g., thing's text profiles are usually short and noise, things are heterogeneous in terms of functionality and attributes) of IoT. In this paper, we develop a novel things categorization technique to automatically predict semantic labels for a given thing. Our proposed approach formulates things categorization as a multi-label classification problem and learns a binary support vector machine classifier for each label to support multi-label classification. We extract two types of features to train classification model: 1) explicit feature from thing's profiles and spatial-temporal pattern; 2) implicit feature from thing's latent relation strength. We utilize a latent variable model to uncover thing's latent relation strength from their interaction behaviours. We conduct a comprehensive experimental study based on three real datasets, and the results show fusing thing's latent relation strength can significantly boost things categorization.
Multitenant Access Control for Cloud-Aware Distributed Filesystems

In a virtualization environment that serves multiple tenants (independent organizations), storage consolidation at the filesystem level is desirable because it enables data sharing, administration efficiency, and performance optimizations. The scalable deployment of filesystems in such environments is challenging due to intermediate translation layers required for networked file access or identity management. First we define the entities involved in a multitenant filesystem and present relevant security requirements. Then we introduce the design of the Dike authorization architecture. It combines native access control with tenant namespace isolation and compatibility to object-based filesystems. We introduce secure protocols to authenticate the participating entities and authorize the data access over the network. We alternatively use a local cluster and a public cloud to experimentally evaluate a Dike prototype implementation that we developed. At several thousand tenants, our prototype incurs limited performance overhead below 21 percent, unlike a solution from industry whose multitenancy overhead approaches 84 percent in some cases.
Normalization of Distance and Similarity in Sequence Analysis

We explore the relations between the notion of distance and a feature set–based concept of similarity and show that this concept of similarity has a spatial interpretation that is complementary to distance: it is interpreted as “direction.” Furthermore, we show how proper normalization leads to distances that can be directly interpreted as dissimilarity: Closeness in normalized space implies and is implied by similarity of the same objects, while remoteness implies and is implied by dissimilarity. Finally, we show how, in research into destandardization of the life course, properly normalizing may drastically and unequivocally change our interpretation of intercohortal distances.