Reviewerly/exHarmony huggingface dataset:
Protected Quantum Teleportation Through Noisy Channel by Weak Measurement and Environment-Assisted Measurement

Quantum teleportation as a key protocol for quantum communication and quantum computing demonstrates the stark difference between quantum and classical information transmission. An ideal teleportation protocol requires pure maximally entangled state as the teleportation channel, while in real implementations the shared entanglement is severely degraded due to decoherence. In this letter, we propose a scheme to protect the quantum teleportation from decoherence by means of environment assisted measurement with weak measurements and flip operations. We show that the teleportation fidelity of our proposed scheme is significantly improved compared to teleportation with no protection and a teleportation protocol based on quantum measurement reversal. Also, the explicit formula of average teleportation fidelity and success probability of the entanglement protection are derived.
Geological Resource Planning and Environmental Impact Assessments Based on GIS

The purpose is to study the geological resource planning and environmental impact assessments based on the geographic information system (GIS). In this study, the land resources of Yinan County in southeastern Shandong Province are taken as the research object. Based on a GIS, the current situation of land resource development is analyzed, land resource planning is carried out, and environmental impact mitigation measures are evaluated and analyzed through the environmental impact. The results obtained depict the distribution of cultivated land; the development area is 1617.31 hm2, of which 577.32 hm2 is cultivated land, 30.43 hm2 is garden land, 399.66 hm2 is forest land, 40.87 hm2 is urban and rural construction land, 10.11 hm2 is traffic water conservancy and other construction land, and 558.92 hm2 is natural reserve land. In the layout of the construction land, the development area is 841.94 hm2, of which 175.44 hm2 is cultivated land, 47.88 hm2 is garden land, 100.54 hm2 is forest land, 0.1 hm2 is other agricultural land, 90.45 hm2 is urban and rural construction land, 3.66 hm2 is traffic water conservancy and other construction land, 11.33 hm2 is water area, and 412.54 hm2 is natural reserve land. The impact of the implementation of planning on most indicators is positive and beneficial, while the impact of negative indicators is relatively small. It is revealed that the implementation of the plan has little impact on most of the ecological environment indicators. Construction and cultivated land development further improve the level of urbanization. In the process of planning implementation, corresponding measures should be taken to slow down or eliminate the negative development of the ecological environment.
Virtual Reality Gaming on the Cloud: A Reality Check

Cloud virtual reality (VR) gaming traffic characteristics such as frame size, inter-arrival time, and latency need to be carefully studied as a first step toward scalable VR cloud service provisioning. To this end, in this paper we analyze the behavior of VR gaming traffic and Quality of Service (QoS) when VR rendering is conducted remotely in the cloud. We first build a VR testbed utilizing a cloud server, a commercial VR headset, and an off-the-shelf WiFi router. Using this testbed, we collect and process cloud VR gaming traffic data from different games under a number of network conditions and fixed and adaptive video encoding schemes. To analyze the application-level characteristics such as video frame size, frame inter-arrival time, frame loss and frame latency, we develop an interval threshold based identification method for video frames. Based on the frame identification results, we present two statistical models that capture the behaviour of the VR gaming video traffic. The models can be used by researchers and practitioners to generate VR traffic models for simulations and experiments - and are paramount in designing advanced radio resource management (RRM) and network optimization for cloud VR gaming services. To the best of the authors&#x0027; knowledge, this is the first measurement study and analysis conducted using a commercial cloud VR gaming platform, and under both fixed and adaptive bitrate streaming. We make our VR traffic datasets publicly available for further research by the community.
Experimental study on the quantum search algorithm over structured datasets using IBMQ experience

In this work, a quantum search algorithm over structured datasets is proposed. Subsequently, the algorithm is executed on a real chip quantum computer developed by IBM Quantum experience (IBMQ). QISKit, the software platform developed by IBM, is used for the implementation of this algorithm. Quantum interference, quantum superposition, and π phase shift of the quantum state are applied in the proposed search algorithm. It performs a π phase shift on the initial state and conducts a state elimination and amplitude amplification process to reach the 'search key' or 'solution key' from the given structured dataset. The proposed quantum algorithm is executed using the QISKit SDK local backend ‘local_qasm_simulator’, real chip 'ibmq_16_melbourne', 'ibmq_belem' and 'ibmqx4′ IBMQ. The results suggest that the real chipibmq_16_melbourne is more quantum error- or noise-prone than ibmq_belem and ibmqx4. The correctness, validation, and application using the proposed algorithm are demonstrated. The algorithm is very promising in terms of low quantum cost and fewer gate requirements. This implies that the proposed quantum search algorithm may be a compelling and pertinent choice in the Noisy Intermediate-Scale Quantum (NISQ) technology era.
Digital Competences of Teachers in the Higher Education Academic Development System: Experience of the Empirical Research

The article analyzes the problem of higher education teachers’ digital competence formation in relation to the university academic development. In the system of academic development, a higher education teacher becomes an active subject and an object of influence of the university structures.The notion of “academic capital” – a system of professional competencies of a teacher, in whose structure digital competencies acquire special importance, is introduced to assess the effectiveness of academic development.It is assumed that depending on the attitudes towards the development and integration of digital competencies into professional practices, teachers are divided into different categories according to their role in the academic development.In order to test the hypothesis, focus group research was carried out with teachers of the humanities, social and natural sciences from universities in different regions of Russia with at least three years of professional experience. A total of five focus groups of seven teachers in each were conducted.In the context of academic development, digital educational practices were analyzed, and three types of agents were identified – innovators and promoters, who contribute to the academic development of the higher education institutions, and routiners, who slow down the introduction of digital technologies. It is concluded that a possible barrier to the innovative activities for teachers is an ambiguous attitude towards the process of digitalization of the education sector.
A novel artificial intelligence approach for regolith geochemical grade prediction using multivariate adaptive regression splines

The necessity for applying a potent analytical regolith geochemical grade estimator is driven by the reality of mineral exploration. This is because many exploration geologists rely upon the classical geostatistical method of Kriging which oftentimes do not produce accurate predictions due to the complexity of interactions between geological features and spatial variables. In this study, a novel non-linear data-driven approach known as Multivariate Adaptive Regression Spline (MARS) is proposed as an effective predictive tool to unravel regolith geochemical complexities. The proposed MARS approach was used to predict regolith geochemical grade from a thick regolith cover in the Tarkwaian paleo-placer of the South-Western Ashanti belt in Ghana. Out of the 891 samples, the data was partitioned into 70% training (model development) and 30% testing (model validation). The proposed MARS result was compared with three other artificial intelligence techniques (i.e., radial basis function neural network, backpropagation neural network and generalised regression neural network) and kriging geostatistical technique. Based on the test results, MARS had the highest correlation coefficient (R = 0.9675) and the least statistical error metrics (RMSE = 0.7791, MAE = 0.6014, and ρ = 0.0472). The implementation of the MARS approach in regolith geochemical grade estimation domain has yielded outstanding and promising results. The MARS superiority was evident in its calibration strength, prediction accuracy, robust interaction of variables and overcoming the black box system of ANN. Thus, the proposed MARS approach could be an excellent tool in regolith geochemical grade estimation workflow when fully integrated with exploration tasks.
Computational intelligence in processing of speech acoustics: a survey

Abstract Speech recognition of a language is a key area in the field of pattern recognition. This paper presents a comprehensive survey on the speech recognition techniques for non-Indian and Indian languages, and compiled some of the computational models used for processing speech acoustics. An immense number of frameworks are available for speech processing and recognition for languages persisting around the globe. However, a limited number of automatic speech recognition systems are available for commercial use. The gap between the languages being spoken around the globe and the technical support available to these languages are very few. This paper examined major challenges for speech recognition for different languages. Analysis of the literature shows that lack of standard databases availability of minority languages hinder the research recognition research across the globe. When compared with non-Indian languages, the research on speech recognition of Indian languages (except Hindi) has not achieved the expected milestone yet. Combination of MFCC and DNN–HMM classifier is most commonly used system for developing ASR minority languages, whereas in some of the majority languages, researchers are using much advance algorithms of DNN. It has also been observed that the research in this field is quite thin and still more research needs to be carried out, particularly in the case of minority languages.
A Privacy-Preserving and Standard-Based Architecture for Secondary Use of Clinical Data

The heterogeneity of the formats and standards of clinical data, which includes both structured, semi-structured, and unstructured data, in addition to the sensitive information contained in them, require the definition of specific approaches that are able to implement methodologies that can permit the extraction of valuable information buried under such data. Although many challenges and issues that have not been fully addressed still exist when this information must be processed and used for further purposes, the most recent techniques based on machine learning and big data analytics can support the information extraction process for the secondary use of clinical data. In particular, these techniques can facilitate the transformation of heterogeneous data into a common standard format. Moreover, they can also be exploited to define anonymization or pseudonymization approaches, respecting the privacy requirements stated in the General Data Protection Regulation, Health Insurance Portability and Accountability Act and other national and regional laws. In fact, compliance with these laws requires that only de-identified clinical and personal data can be processed for secondary analyses, in particular when data is shared or exchanged across different institutions. This work proposes a modular architecture capable of collecting clinical data from heterogeneous sources and transforming them into useful data for secondary uses, such as research, governance, and medical education purposes. The proposed architecture is able to exploit appropriate modules and algorithms, carry out transformations (pseudonymization and standardization) required to use data for the second purposes, as well as provide efficient tools to facilitate the retrieval and analysis processes. Preliminary experimental tests show good accuracy in terms of quantitative evaluations.
Professional-pedagogical staff: new training techniques

The article deals with the issues of training professional and pedagogical personnel for the system of vocational education, analyzes the methods used to attract teaching staff in the USSR in the 1970s–1980s and in the system of intra-company corporate training in the modern period. Determining potential candidates for involvement in teaching activities in technical schools and colleges of the country, special emphasis is proposed to be placed on employees of industrial enterprises as well as winners and prizewinners of professional skills championships. The involvement of such categories of personnel requires the development of innovative education techniques that allow to eliminate professional deficits in the field of pedagogical and psychological knowledge effectively and in the shortest possible time. The personalized learning model is presented.
ICON: Learning Regular Maps Through Inverse Consistency

Learning maps between data samples is fundamental. Applications range from representation learning, image translation and generative modeling, to the estimation of spatial deformations. Such maps relate feature vectors, or map between feature spaces. Well-behaved maps should be regular, which can be imposed explicitly or may emanate from the data itself. We explore what induces regularity for spatial transformations, e.g., when computing image registrations. Classical optimization-based models compute maps between pairs of samples and rely on an appropriate regularizer for well-posedness. Recent deep learning approaches have attempted to avoid using such regularizers altogether by relying on the sample population instead. We explore if it is possible to obtain spatial regularity using an inverse consistency loss only and elucidate what explains map regularity in such a context. We find that deep networks combined with an inverse consistency loss and randomized off-grid interpolation yield well behaved, approximately diffeomorphic, spatial transformations. Despite the simplicity of this approach, our experiments present compelling evidence, on both synthetic and real data, that regular maps can be obtained without carefully tuned explicit regularizers, while achieving competitive registration performance.
A Novel Framework for Arabic Dialect Chatbot Using Machine Learning

With the advent of artificial intelligence and proliferation in the demand for an online dialogue system, the popularity of chatbots is growing on various industrial platforms. Their applications are getting widely noticed with intelligent tools as they are able to mimic human behavior in natural languages. Chatbots have been proven successful for many languages, such as English, Spanish, and French, over the years in varied fields like entertainment, medicine, education, and commerce. However, Arabic chatbots are challenging and are scarce, especially in the maintenance domain. Therefore, this research proposes a novel framework for an Arabic troubleshooting chatbot aiming at diagnosing and solving technical issues. The framework addresses the difficulty of using the Arabic language and the shortage of Arabic chatbot content. This research presents a realistic implementation of creating an Arabic corpus for the chatbot using the developed framework. The corpus is developed by extracting IT problems/solutions from multiple domains and reliable sources. The implementation is carried forward towards solving specific technical solutions from customer support websites taken from different well-known organizations such as Samsung, HP, and Microsoft. The claims are proved by evaluating and conducting experiments on the dataset by comparing with the previous researches done in this field using different metrics. Further, the validations are well presented by the proposed system that outperforms the previously developed different types of chatbots in terms of several parameters such as accuracy, response time, dataset data, and solutions given as per the user input.
Dual-Cameras-Based Driver’s Eye Gaze Tracking System with Non-Linear Gaze Point Refinement

The human eye gaze plays a vital role in monitoring people's attention, and various efforts have been made to improve in-vehicle driver gaze tracking systems. Most of them build the specific gaze estimation model by pre-annotated data training in an offline way. These systems usually tend to have poor generalization performance during the online gaze prediction, which is caused by the estimation bias between the training domain and the deployment domain, making the predicted gaze points shift from their correct location. To solve this problem, a novel driver's eye gaze tracking method with non-linear gaze point refinement is proposed in a monitoring system using two cameras, which eliminates the estimation bias and implicitly fine-tunes the gaze points. Supported by the two-stage gaze point clustering algorithm, the non-linear gaze point refinement method can gradually extract the representative gaze points of the forward and mirror gaze zone and establish the non-linear gaze point re-mapping relationship. In addition, the Unscented Kalman filter is utilized to track the driver's continuous status features. Experimental results show that the non-linear gaze point refinement method outperforms several previous gaze calibration and gaze mapping methods, and improves the gaze estimation accuracy even on the cross-subject evaluation. The system can be used for predicting the driver's attention.
Privacy-preserving training of tree ensembles over continuous data

Abstract Most existing Secure Multi-Party Computation (MPC) protocols for privacy-preserving training of decision trees over distributed data assume that the features are categorical. In real-life applications, features are often numerical. The standard “in the clear” algorithm to grow decision trees on data with continuous values requires sorting of training examples for each feature in the quest for an optimal cut-point in the range of feature values in each node. Sorting is an expensive operation in MPC, hence finding secure protocols that avoid such an expensive step is a relevant problem in privacy-preserving machine learning. In this paper we propose three more efficient alternatives for secure training of decision tree based models on data with continuous features, namely: (1) secure discretization of the data, followed by secure training of a decision tree over the discretized data; (2) secure discretization of the data, followed by secure training of a random forest over the discretized data; and (3) secure training of extremely randomized trees (“extra-trees”) on the original data. Approaches (2) and (3) both involve randomizing feature choices. In addition, in approach (3) cut-points are chosen randomly as well, thereby alleviating the need to sort or to discretize the data up front. We implemented all proposed solutions in the semi-honest setting with additive secret sharing based MPC. In addition to mathematically proving that all proposed approaches are correct and secure, we experimentally evaluated and compared them in terms of classification accuracy and runtime. We privately train tree ensembles over data sets with thousands of instances or features in a few minutes, with accuracies that are at par with those obtained in the clear. This makes our solution more efficient than the existing approaches, which are based on oblivious sorting.
FabLabs: The Road to Distributed and Sustainable Technological Training through Digital Manufacturing

The fast expansion of digital culture has fostered the creation of makerspaces such as fabrication laboratories (FabLabs) that, thanks to their flexibility and their use of open source tools, strengthen the sense of community and produce true transformations within those communities. Despite their relevance, few studies focus on the characterization of these environments. This paper presents the results of the FabLab Global Survey, aimed at understanding the characteristics of FabLabs through the visions of their managers, or “FabManagers”. The results show an enormous diversity of approaches within the FabLab movement that cannot be extrapolated to a single characteristic element, but that allow its global compression. Their properties reveal them as transforming elements that eliminate technological and cultural barriers, empowering user communities and optimizing learning processes regarding digital technology. FabLab activities allow not only economic and industrial development thanks to innovative projects, but also a digital technology approach for young students and the inclusion of minorities at risk, thus eliminating old cultural and social barriers.
A time-delay signature elimination and broadband electro-optic chaotic system with enhanced nonlinearity by deep learning

In this paper, a novel electro-optic chaotic system with enhanced nonlinearity by deep learning (ENDL) is proposed to achieve time-delay signature (TDS) elimination. A long-short term memory network (LSTM) is trained by a specially designed loss function to enhance the nonlinear effect that can hide the TDS of the system. For the first time, the trained deep learning module is put into a single feedback loop to participate in chaos generation. Simulation results show that the ENDL system can eliminate TDS and increase the bandwidth to more than 31GHz when the feedback intensity is very low (α = 4V). Moreover, the complexity of the chaotic output can be improved with permutation entropy (PE) reaching 0.9941. The synchronization result shows that the ENDL system has high sensitivity to TDS but has low sensitivity to the feedback intensity, thus the system has both high security and high robustness. This system has an uncomplicated synchronization structure and high flexibility, and it opens up a new direction for high-quality chaos generation.
IUPAC specification for the FAIR management of spectroscopic data in chemistry (IUPAC FAIRSpec) – guiding principles

Abstract A set of guiding principles for the development of a standard for FAIR management of spectroscopic data are outlined and discussed. The principles form the basis for future recommendations of IUPAC Project 2019-031-1-024 specifying a detailed data model and metadata schema for describing the contents of an “IUPAC FAIRData Collection” and the organization of digital objects within that collection. Foremost among the recommendations will be a specification for an “IUPAC FAIRData Finding Aid” that describes the collection in such a way as to optimize the findability , accessibility , interoperability , and reusability of its contents. Results of an analysis of data provided by an American Chemical Society Publications pilot study are discussed in relation to potential workflows that might be used in implementing the “IUPAC FAIRSpec” standard based on these principles.