Reviewerly/exHarmony huggingface dataset:
Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

The exponential rise in advanced software computing and low-cost hardware has broadened the horizon for the Internet of Medical Things (IoMT), interoperable e-Healthcare systems serving varied purposes including electronic healthcare records (EHRs) and telemedicine. However, being heterogeneous and dynamic in nature, their database security remains a challenge forever. Numerous intrusion attacks including bot-attack and malware have confined major classical databases towards e-Healthcare. Despite the robustness of NoSQL over the structured query language databases, the dynamic data nature over a heterogeneous environment makes it vulnerable to intrusion attacks, especially over interoperable e-Healthcare systems. Considering these challenges, this work proposed a first of its kind semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model for interoperable e-Healthcare systems. This work assessed the efficacy of the different semantic feature-extraction methods like Word2Vec, Continuous Bag of Words, N-Skip Gram (SKG), Count Vectorizer, TF-IDF, and GLOVE towards NoSQL-IA prediction. Subsequently, to minimize computational exhaustion, different feature selection methods including Wilcoxon Rank Sum Test (WRST), significant predictor test, principal component analysis, Select K-Best, and variance threshold feature selection algorithms were employed. To alleviate the data imbalance problem, it applied different resampling methods including upsampling, downsampling, and synthetic minority oversampling technique (SMOTE) over the selected features. Later, Min–Max normalization was performed over the input feature vectors to alleviate any possibility of overfitting. Towards NoSQL-IA prediction, different machine learning methods like Multinomial Naïve Bayes, decision tree, logistic regression, support vector machine, k-NN, AdaBoost, Extra Tree Classifier, random forest ensemble, and XG-Boost were applied, which classified each input query as the regular query or the NoSQL-IA attack query. The depth performance assessment revealed that the use of Word2Vec features SKG in sync with VTFS feature selection and SMOTE resampling processed with the bootstrapped random forest classifier can provide the best performance in terms of high accuracy (98.86%), F-Measure (0.974), and area under the curve (AUC) (0.981), thus enabling it suitable for interoperable e-Healthcare database security.
The Decoherence-Free Subalgebra of Gaussian Quantum Markov Semigroups

We demonstrate a method for finding the decoherence-subalgebra $\mathcal{N}(\mathcal{T})$ of a Gaussian quantum Markov semigroup on the von Neumann algebra $\mathcal{B}(\Gamma(\mathbb{C}^d))$ of all bounded operator on the Fock space $\Gamma(\mathbb{C}^d)$ on $\mathbb{C}^d$. We show that $\mathcal{N}(\mathcal{T})$ is a type I von Neumann algebra $L^\infty(\mathbb{R}^{d_c};\mathbb{C})\bar{\otimes}\mathcal{B}(\Gamma(\mathbb{C}^{d_f}))$ determined, up to unitary equivalence, by two natural numbers $d_c,d_f\leq d$. This result is illustrated by some applications and examples.
The importance of choosing the right keywords for educational technology publications

Keywords refer to important words or concepts that represent the research foci and theoretical backgrounds of an academic study. They enable readers to glean a quick impression about what they are going to read from an academic article. Keywords also provide valuable information for researchers who intend to search for articles related to a particular field or conduct a survey related to a specific topic. Therefore, in selected academic journals, detailed guidelines are provided to help authors choose appropriate keywords for highlighting their research. In this editorial, we examine the role of keywords from several perspectives by reviewing the keywords adopted by AJET authors in recent years. Accordingly, we attempt to provide recommendations to AJET authors for their future submissions. It is expected that, via using proper keywords, the authors and readers as well as the journal can be benefited.
User requirements for the design of smart homes: dimensions and goals

Abstract The ‘Smart Home’ is a strongly technology-driven field. While user-centered requirements have been reported for specific features, a considerable gap persists for design based on an everyday home context and the social and emotional nature of the home. To address this, we present a user-centered design process to question and expand narrow framings of energy-efficiency and smart control and consider the richness and variety of the domestic context as design space for smart homes. Our three-step investigation employs cultural probing, participatory design fiction, and focus groups to progress from the home context “as-is” towards a blending of values with technological responses. Our findings highlight the home as a complex construct imbued with organically grown practices and individual and collective needs, values, and emotions. Based on empirical, real-user data we present features and system expectations that address this multifaceted overall picture. This paper advises the design process of future smart home solutions in three facets: first, we discuss the value of the design process applied in this study and future possibilities to expand. Second, we show design dimensions , namely time, space, relations, individual factors, and values that allow design for a heterogeneity of users and situations. Third, we derive specific design goals to highlight directions of smart home system design: design for control, low effort, integration, evolvability, identity, sociability, and benefits.
Salp swarm algorithm based optimal speed control for electric vehicles

&lt;p&gt;The paper is all about the implementation of a novel bio-inspired metaheuristic&lt;br /&gt;salp swarm algorithm (SSA) for speed control of brushless DC&lt;br /&gt;(BLDC) motor drive that is run in sensorless control mode. The angular&lt;br /&gt;speed of the motor is evaluated using an extended kalman filter, in which the&lt;br /&gt;dynamics of the motor are nonlinear. The error in speeds between actual and&lt;br /&gt;estimated is fed to the PID controller. To achieve the good transient&lt;br /&gt;operation of the motor drive, the parameters of the PID are tuned with the&lt;br /&gt;SSA. The optimum PID gains are determined by the minimization of integral&lt;br /&gt;square error and then final optimum gains are validated on the laboratory&lt;br /&gt;testbed. The proposed method is also tested in various cases to check the&lt;br /&gt;performance of the drive. The experiments are also performed at low speeds&lt;br /&gt;to know the superiority of the proposed method.&lt;/p&gt;
Stitching interferometry of cylindrical surface

• A new strategy of stitching interferometry for cylindricity calibration is proposed. • A two-dimensional Gaussian filter weighting function is designed for extracting the cylindrical profile. • The power spectrum density maps in two directions are also given to reflect the frequency characteristic of the profile. • The uncertainty and the confidence interval of the stitching result are analyzed. • The comparison of the result of stitching interferometry and cylindricity measuring instrument is analyzed. High resolution and high precision are advantages of measuring cylindrical objects employing stitching interferometry. However, due to errors in the cylinder position in a measurement procedure, it is difficult to ensure the accuracy of a macroscopic shape contour and surface waviness contour of the workpiece of the stitching results. To overcome this problem, we proposed a new strategy of stitching interferometry for cylindricity calibration. The measuring process is divided into two parts - to measure the macroscopic shape and waviness contour, respectively. Finally, the two measurement results are merged to produce an accurate and complete 360° form map. Besides, we build a two-dimensional Gaussian filter weighting function for a cylindrical profile according to its special shape characteristic. To express the stitching results more precisely, we provide the uncertainty map and power spectral density of the stitching results. An experiment is carried out by taking a ceramic plug gage as the workpiece. The results demonstrate the excellent performance of our method in analyzing the characteristic of the cylindrical profile and show good agreement with such by using cylindricity measuring instruments.
Improved MITM Cryptanalysis on Streebog

At ASIACRYPT 2012, Sasaki et al. introduced the guess-and-determine approach to extend the meet-in-the-middle (MITM) preimage attack. At CRYPTO 2021, Dong et al. proposed a technique to derive the solution spaces of nonlinear constrained neutral words in the MITM preimage attack. In this paper, we try to combine these two techniques to further improve the MITM preimage attacks. Based on the previous MILP-based automatic tools for MITM attacks, we introduce new constraints due to the combination of guess-and-determine and nonlinearly constrained neutral words to build a new automatic model.As a proof of work, we apply it to the Russian national standard hash function Streebog, which is also an ISO standard. We find the first 8.5-round preimage attack on Streebog-512 compression function and the first 7.5-round preimage attack on Streebog-256 compression function. In addition, we give the 8.5-round preimage attack on Streebog-512 hash function. Our attacks extend the best previous attacks by one round. We also improve the time complexity of the 7.5-round preimage attack on Streebog-512 hash function and 6.5-round preimage attack on Streebog-256 hash function.
Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems

In this paper, we present Duplex Conversation, a multi-turn, multimodal spoken dialogue system that enables telephone-based agents to interact with customers like a human. We use the concept of full-duplex in telecommunication to demonstrate what a human-like interactive experience should be and how to achieve smooth turn-taking through three subtasks: user state detection, backchannel selection, and barge-in detection. Besides, we propose semi-supervised learning with multimodal data augmentation to leverage unlabeled data to increase model generalization. Experimental results on three sub-tasks show that the proposed method achieves consistent improvements compared with baselines. We deploy the Duplex Conversation to Alibaba intelligent customer service and share lessons learned in production. Online A/B experiments show that the proposed system can significantly reduce response latency by 50%.
Path–orbital-angular-momentum high-dimensional hyperentangled photons from a warm atomic ensemble

We experimentally demonstrate a scheme for the preparation of high-dimensional hyperentangled photon pairs with path degree of freedom (DOF) and orbital-angular-momentum (OAM) DOF using beam displacer (BD) interferometers from a warm atomic ensemble. A strong Gaussian pump beam of fixed frequency passes through the BD to produce a superposition state of two paths. The biphoton state is generated from two parallel spontaneous four-wave mixing processes in the $^{85}\mathrm{Rb}$ vapor cell. We successfully detect the path entanglement, two-dimensional OAM entanglement, and three-dimensional OAM entanglement by selecting the appropriate measurement. Due to the advantages of the simplicity of our interaction system and high dimensionality of the narrowband hyperentangled photons we generated, such hyperentangled source from an atomic ensemble can be utilized to realize high-dimensional multiple-DOF quantum memory and quantum communication in the future.
Virtual reality content creation based on self-contained components in the e-learning domain: Re-using pattern-based vr content in different authoring toolkits

Abstract In the context of e-learning, it is challenging to incorporate emerging technologies, such as alternate reality games or Virtual Reality (VR), within current learning trends. Microlearning is such a current trend. It divides large and complex chunks of content into small and elementary learning nuggets. These single self-contained nuggets are then composed to overarching lessons or courses. The concept of VR nuggets dovetails this educational trend. VR nuggets are standalone, self-contained, and rather short VR experiences that can be combined with other learning nuggets. By using initial implementations of VR nuggets, they can be used to let authors create VR earning content, for example, to let learners experience alternate realities. In this paper, we further investigate the VR nugget authoring concept and extent it. We introduce two novel authoring toolkits that rely on VR nuggets – one based on context-related module interaction ( CoNMoD ) and one based on visual scripting ( ViNS Tiles ). In two separate user studies, we examine the acceptance of the toolkits and compare them to existing authoring environments that also rely on VR nuggets but utilize different interface techniques. These studies’ results emphasize the importance of exchanging content between different established tools and indicate the acceptance of our tools regarding their hedonic and pragmatic qualities, also compared to existing tools from related work. As a conclusion, we propose an exchange format for VR nuggets that supports their reusability. It enables authors that use different toolkits to work together. They can utilize VR nuggets created with other toolkits and still use their own preferred toolkit. By means of an expert survey, we draw conclusions on technical aspects and a suitable platform to make VR nuggets available to the community. This survey indicates that potential authors would use such an exchange-approach for creating and presenting VR content and that they are willing to share their work and to contribute in a VR nugget authoring community.
Perceptual Conversational Head Generation with Regularized Driver and Enhanced Renderer

This paper reports our solution for ACM Multimedia ViCo 2022 Conversational Head Generation Challenge, which aims to generate vivid face-to-face conversation videos based on audio and reference images. Our solution focuses on training a generalized audio-to-head driver using regularization and assembling a high-visual quality renderer. We carefully tweak the audio-to-behavior model and post-process the generated video using our foreground-background fusion module. We get first place in the listening head generation track and second place in the talking head generation track on the official leaderboard. Our code is available at https://github.com/megvii-research/MM2022-ViCoPerceptualHeadGeneration.
Efficient generation of spin cat states with twist-and-turn dynamics via machine optimization

Spin cat states are promising candidates for achieving Heisenberg-limited quantum metrology. It is suggested that spin cat states can be generated by adiabatic evolution. However, due to the limited coherence time, the adiabatic process may be too slow to be practical. To speed up the state generation, we propose to use machine optimization to generate desired spin cat states. Our proposed scheme relies only on experimentally demonstrated one-axis twisting interactions with piecewise time-modulation of rotations designed via machine optimization. The required evolution time is much shorter than the one with adiabatic evolution and it does not make large modification to the existing experimental setups. Our protocol with machine optimization is efficient and easy to be implemented in state-of-the-art experiments.
Binary and multinomial classification through evolutionary symbolic regression

We present three evolutionary symbolic regression-based classification algorithms for binary and multinomial datasets: GPLearnClf, CartesianClf, and ClaSyCo. Tested over 162 datasets and compared to three state-of-the-art machine learning algorithms -- XGBoost, LightGBM, and a deep neural network -- we find our algorithms to be competitive. Further, we demonstrate how to find the best method for one's dataset automatically, through the use of a state-of-the-art hyperparameter optimizer.
EPQuant: A Graph Neural Network compression approach based on product quantization

Graph Neural Networks (GNNs) have been widely used in graph analysis due to their strong performance on a wide variety of tasks. Unfortunately, as the size of graphs keeps growing, large graphs can easily consume Terabytes, and training on such graphs may take days. The high memory footprint limits the usage of GNNs on resource-constrained devices like smartphones and IoT devices. Hence, reducing the storage cost, training time, and inference latency is highly desirable. In this work, we apply Product Quantization (PQ) on GNNs for the first time to achieve superior memory capacity reduction. To alleviate the processing burden caused by PQ and improve compression performance, we propose Enhanced Product Quantization (EPQ). It reduces the input graph data, which tends to dominate memory consumption, and accelerates clustering in PQ. Moreover, an efficient quantization framework1 for GNNs is proposed, which combines EPQ with Scalar Quantization (SQ), to achieve improved compression performance and computation acceleration on off-the-shelf hardware, enabling the deployment of GNNs on resource-constrained devices. In addition, the proposed quantization framework can be applied to the existing GNNs without too much porting effort. Extensive experimental results show that the proposed quantization scheme can achieve 321.26× and 184.03× memory capacity compression for input graph data and overall storage, respectively. This impressive memory reduction can come with an accuracy loss of less than 1%.
A hybrid web analytic approach through click enabled vision based page segmentation in quest software for school students

The primary goal of this study is to optimize web content for a positive user experience and to develop a data-driven methodology to assess the success of visitor flow on a website for school children. Through Vision-Based Page Segmentation, the suggested study work intends to broaden the stated web approach’s reach and statistical inference. The improvisation has been made accordingly with the semantic structure observed from each node with the designated degree of coherence to indicate the content in spatial and block based on visual perception for each event. The click count (number of clicks) is calculated for all the possibilities of Quest Software. The most frequently accessed event is displayed at the top to enhance usability and visibility with an accuracy of about 92.80%. From the experimental analysis, it has been observed that most of the students preferred events corresponding to drawing, rhymes, and rangoli with a willingness rate of above 80%, respectively. Statistical analysis has been made using chi-square analysis, and it has been found that the levels from A to D are significant for three years with a P-value &lt; 0.001. Sentimental analysis of feedback collected from the participants about the events is also done, and the most preferred event is suggested for the upcoming years.
Educational Data Mining Using Cluster Analysis Methods and Decision Trees based on Log Mining

Educational Data Mining (EDM) often appears to be applied in big data processing in the education sector. One of the educational data that can be further processed with EDM is activity log data from an e-learning system used in teaching and learning activities. The log activity can be further processed more specifically by using log mining. The purpose of this study was to process log data from the Sebelas Maret University Online Learning System (SPADA UNS) to determine student learning behavior patterns and their relationship to the final results obtained. The data mining method applied in this research is cluster analysis with the K-means Clustering and Decision Tree algorithms. The clustering process is used to find groups of students who have similar learning patterns. While the decision tree is used to model the results of the clustering in order to enable the analysis and decision-making processes. Processing of 11,139 SPADA UNS log data resulted in 3 clusters with a Davies Bouldin Index (DBI) value of 0.229. The results of these three clusters are modeled by using a Decision Tree. The decision tree model in cluster 0 represents a group of students who have a low tendency of learning behavior patterns with the highest frequency of access to course viewing activities obtained accuracy of 74.42% . In cluster 1, which contains groups of students with high learning behavior patterns, have a high frequency of access to viewing discussion activities obtained accuracy of 76.47%. While cluster 2 is a group of students who have a pattern of learning behavior that is having a high frequency of access to the activity of sending assignments obtained accuracy of 90.00%.&#x0D;
Reservoir-engineering shortcuts to adiabaticity

We propose a protocol that achieves fast adiabatic transfer between two orthogonal states of a qubit by coupling with an ancilla. The qubit undergoes Landau-Zener dynamics, whereas the coupling realizes a time-dependent Hamiltonian, which is diagonal in the spin's instantaneous Landau-Zener eigenstates. The ancilla (or meter), in turn, couples to a thermal bath, such that the overall dynamics is incoherent. We analyse the protocol's fidelity as a function of the strength of the coupling and of the relaxation rate of the meter. When the meter's decay rate is the largest frequency scale of the dynamics, the spin dynamics is encompassed by a master equation describing dephasing of the spin in the instantaneous eigenbasis. In this regime the fidelity of adiabatic transfer improves as the bath temperature is increased. Surprisingly, the adiabatic transfer is significantly more efficient in the opposite regime, where the time scale of the ancilla dynamics is comparable to the characteristic spin time scale. Here, for low temperatures the coupling with the ancilla tends to suppress diabatic transitions via effective cooling. The protocol can be efficiently implemented by means of a pulsed, stroboscopic coupling with the ancilla and is robust against moderate fluctuations of the experimental parameters.
What Makes a Strong Monad?

Strong monads are important for several applications, in particular, in the denotational semantics of effectful languages, where strength is needed to sequence computations that have free variables. Strength is non-trivial: it can be difficult to determine whether a monad has any strength at all, and monads can be strong in multiple ways. We therefore review some of the most important known facts about strength and prove some new ones. In particular, we present a number of equivalent characterizations of strong functor and strong monad, and give some conditions that guarantee existence or uniqueness of strengths. We look at strength from three different perspectives: actions of a monoidal category V, enrichment over V, and powering over V. We are primarily motivated by semantics of effects, but the results are also useful in other contexts.
SparCAssist

We introduce SparCAssist, a general-purpose risk assessment tool for the machine learning models trained for language tasks. It evaluates models' risk by inspecting their behavior on counterfactuals, namely out-of-distribution instances generated based on the given data instance. The counterfactuals are generated by replacing tokens in rational subsequences identified by ExPred, while the replacements are retrieved using HotFlip or the Masked-Language-Model-based algorithms. The main purpose of our system is to help the human annotators to assess the model's risk on deployment. The counterfactual instances generated during the assessment are the by-product and can be used to train more robust NLP models in the future.