Reviewerly/exHarmony huggingface dataset:
Characterizing qubit channels in the context of quantum teleportation

We consider a scenario where a party, say, Alice prepares a pure two-qubit (either maximally entangled or nonmaximally entangled) state and sends one half of this state to another distant party, say, Bob through a qubit (either unital or nonunital) channel. Finally, the shared state is used as a teleportation channel. In this scenario, we focus on characterizing the set of qubit channels with respect to the final state's efficacy as a resource of quantum teleportation (QT) in terms of maximal average fidelity and fidelity deviation (fluctuation in fidelity values over the input states). Importantly, we point out the existence of a subset of qubit channels for which the final state becomes useful for universal QT (having maximal average fidelity strictly greater than the classical bound and having zero fidelity deviation) when the initially prepared state is either useful for universal QT (i.e., for a maximally entangled state) or not useful for universal QT (i.e., for a subset of nonmaximally entangled pure states). Interestingly, in the latter case, we show that nonunital channels (dissipative interactions) are more effective than unital channels (nondissipative interactions) in producing useful states for universal QT from nonmaximally entangled pure states.
An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification

The multi-scale lightweight network and attention mechanism recently attracted attention in person re-identification (ReID) as it is capable of improving the model’s ability to process information with low computational cost. However, state-of-the-art methods mostly concentrate on the spatial attention and big block channel attention model with high computational complexity while rarely investigate the inside block attention with the lightweight network, which cannot meet the requirements of high efficiency and low latency in the actual ReID system. In this paper, a novel lightweight person ReID model is designed firstly, called Multi-Scale Focusing Attention Network (MSFANet), to capture robust and elaborate multi-scale ReID features, which have fewer float-computing and higher performance. MSFANet is achieved by designing a multi-branch depthwise separable convolution module, combining with an inside block attention module, to extract and fuse multi-scale features independently. In addition, we design a multi-stage backbone with the ‘1-2-3’ form, which can significantly reduce computational cost. Furthermore, the MSFANet is exceptionally lightweight and can be embedded in a ReID framework flexibly. Secondly, an efficient loss function combining softmax loss and TriHard loss, based on the proposed optimal data augmentation method, is designed for faster convergence and better model generalization ability. Finally, the experimental results on two big ReID datasets (Market1501 and DukeMTMC) and two small ReID datasets (VIPeR, GRID) show that the proposed MSFANet achieves the best mAP performance and the lowest computational complexity compared with state-of-the-art methods, which are increasing by 2.3% and decreasing by 18.2%, respectively.
A Novel Recurrent Neural Networks Architecture for Behavior Analysis

Behavior analysis is an important yet challenging task on computer vision area. However, human behavior is still a necessity in differents sectors. In fact, in the increase of crimes, everyone needs video surveillance to keep their belongings safe and to automatically detect events by collecting important information for the assistance of security guards. Moreover, the surveillance of human behavior is recently used in medicine fields to quickly detect physical and mental health problems of patients. The complex and the variety presentation of human features in video sequence encourage researches to find the effective presentation. An effective presentation is the most challenging part. It must be invariant to changes of point of view, robust to noise and efficient with a low computation time. In this paper, we propose new model for human behavior analysis which combine transfer learning model and Recurrent Neural Network (RNN). Our model can extract human features from frames using the pre-trained model of Convolutional Neural Network (CNN) the Inception V3. The human features obtained are trained using RNN with Gated Recurrent Unit (GRU). The performance of our proposed architecture is evaluated by three different dataset for human action, UCF Sport, UCF101 and KTH, and achieved good classification accuracy
General geology and geochemistry of the Lokpanta Formation oil shale, Nigeria

A detailed geochemical and mineralogical study was carried out on the Lokpanta oil shale (OS), Nigeria.Samples from three drill cores and several outcrops were studied in order to understand OS general geochemistry and reconstruct tectonic and depositional settings as well as paleoredox conditions.The mineral phases in OS include calcite, quartz, dolomite, feldspar, illite, kaolinite, halloysite, pyrite and gypsum, as well as a trace amount of anatase.The Lokpanta oil shale shows little variation in geochemistry.It is depleted in trace elements Ba and Rb and major compounds except CaO, and is enriched in trace elements Mo, Sb, As, V, Zn, Ni and U with reference to the Post-Archean Australian Shale (PAAS).These enrichments are, however, in most cases lower than those in the Estonian graptolite argillite (GA).Trace element ratios (U/Th, Ni/Co, V/Ni, V/(V + Ni) V/(V + Cr)) indicate that the Lokpanta oil shale was deposited in an anoxic environment.Discriminant diagrams also suggest its deposition in an active continental margin setting and a transitional to marine environment.The geochemical and paleoenvironmental features of the Lokpanta oil shale were compared with those of the Estonian graptolite argillite.
Towards augmented reality for corporate training

Corporate training relates to employees acquiring essential skills to operate equipment or effectively performing required tasks both competently and safely. Unlike formal education, training can be incorporated into the task workflow and performed during working hours. Increasingly, organizations adopt different technologies to develop both individual skills and improve their organization. Studies indicate that Augmented Reality (AR) is quickly becoming an effective technology for training programs. This systematic literature review (SLR) aims to screen works published on AR for corporate training. We describe AR training applications, discuss current challenges, literature gaps, opportunities, and tendencies of corporate AR solutions. We structured a protocol to define keywords, the semantics of research, and databases used as sources of this SLR. From a primary analysis, we considered 1952 articles in the review for qualitative synthesis. We selected 60 among the selected articles for this study. The survey shows a large number of 41.7% of applications focused on automotive and medical training. Additionally, 20% of selected publications use a camera-display with a tablet device, while 40% refer to head-mounted-displays, and many surveyed approaches (45%) adopt marker-based tracking. Results indicate that publications on AR for corporate training increased significantly in recent years. AR has been used in many areas, exhibiting high quality and provides viable approaches to On-The-Job training. Finally, we discuss future research issues related to increasing relevance regarding AR for corporate training.
Modelling COVID-19 using Fuzzy Cognitive Maps (FCM)

INTRODUCTION: The outbreak of COVID-19 has gained ground in many countries, leading towards a global health emergency. Research efforts have been intensified all around the humankind. OBJECTIVE: The main objective of this paper is to study the new pandemic COVID-19 using for the first-time theori
Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent

Byzantine-resilient Stochastic Gradient Descent (SGD) aims at shielding model training from Byzantine faults, be they ill-labeled training datapoints, exploited software/hardware vulnerabilities, or malicious worker nodes in a distributed setting. Two recent attacks have been challenging state-of-the-art defenses though, often successfully precluding the model from even fitting the training set. The main identified weakness in current defenses is their requirement of a sufficiently low variance-norm ratio for the stochastic gradients. We propose a practical method which, despite increasing the variance, reduces the variance-norm ratio, mitigating the identified weakness. We assess the effectiveness of our method over 736 different training configurations, comprising the 2 state-of-the-art attacks and 6 defenses. For confidence and reproducibility purposes, each configuration is run 5 times with specified seeds (1 to 5), totalling 3680 runs. In our experiments, when the attack is effective enough to decrease the highest observed top-1 cross-accuracy by at least 20% compared to the unattacked run, our technique systematically increases back the highest observed accuracy, and is able to recover at least 20% in more than 60% of the cases.
Whale Optimization Algorithm For Solving Association Rule Mining Issue

Our-days, with the significant number of connected devices, data stored has grown significantly.The exploitation of the information stored in it for decision-making has become indispensable in the most prominent companies.Association rule mining draws in consideration of researchers to extract relationships between items in data.Nevertheless, this process is costly in terms of memory and computation, Which are the foremost drawbacks in exact algorithms designed for mining association rule.Recently, swarm-based algorithms prove their efficiency in data mining.With this in mind, an updated whale optimization algorithm to mine association rules, called (WO-ARM), proposed in this paper.Whale optimization has a good trade-off between intensification and diversification, which inherited by our proposal.A set of tests carried out on different famous benchmarks.The preliminary results show that our approach beats other swarm inspired algorithms already exist in the literature in terms of quality, runtime, and memory usage.
Inclusion–exclusion principle for open quantum systems with bosonic bath

We present two fast algorithms which apply inclusion-exclusion principle to sum over the bosonic diagrams in bare diagrammatic quantum Monte Carlo (dQMC) and inchworm Monte Carlo method, respectively. In the case of inchworm Monte Carlo, the proposed fast algorithm gives an extension to the work ["Inclusion-exclusion principle for many-body diagrammatics", Phys. Rev. B, 98:115152, 2018] from fermionic to bosonic systems. We prove that the proposed fast algorithms reduce the computational complexity from double factorial to exponential. Numerical experiments are carried out to verify the theoretical results and to compare the efficiency of the methods.
Application of Human Factors in the Development Process of Immersive Visual Technologies: Challenges and Future Improvements

This study investigates how Human Factors (HF) is applied when designing and developing Immersive Visual Technologies (IVT), including Augmented Reality, Mixed Reality, and Virtual Reality. We interviewed fourteen people working at different organizations, that develop IVT applications in the Nordic region. We used thematic analysis to derive themes from the interviews. The results showed an insufficient knowledge and application of HF in IVT development, due to the lack of awareness of both scope and significance of HF, resource allocation strategy, market inertia, stakeholder's involvement, standardization of HF application and IVT uses, and technology maturity. This situation could be improved by allocating experts, adjusting organizational strategy to balance resource allocation, training developers and user organizations to raise awareness and to encourage co-creative design and knowledge sharing, create a sense of ownership amongst stakeholders, and ensure the usefulness of the technology to the user's work.
COVID-19 and the inclusion of learners with disabilities in basic education in South Africa: A critical analysis

This article examines the extent to which basic education, which is compulsory under international law, was inclusive of learners with disabilities during the COVID-19 pandemic. To this end, it examines measures taken by the government to ensure the continuity of basic education and the extent to which these measures are inclusive of learners with disabilities. It argues that moving education to online platforms, and conducting classes via radio and television are not accompanied by related reasonable accommodation measures to ensure the inclusion of leaners with disabilities. Among others, study material and numerous resources, online platforms and media are not in accessible formats, and learners with disabilities do not have access to data or internet broadband. In addition, the parents of these learners with disabilities are not trained to assist their children to study from home. The exclusion from school of learners with special needs is also characterised by a limited number of special schools in the country. In making its case, the article relies on South African and foreign jurisprudence on equality and inclusive education to inform the analysis. Ultimately the article finds that learners with disabilities are not included in the education system in the time of COVID-19. It explores general lessons learned during the pandemic which could be considered as an opportunity to re-think how emergency education planning can be inclusive of children or learners with disabilities in the future. While the discussion focuses on South Africa, lessons learned apply across Africa where persons with disabilities generally are marginalised.
3D DCT Based Image Compression Method for the Medical Endoscopic Application

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.
A New Quantitative Method for Risk Assessment of Coal Floor Water Inrush Based on PSR Theory and Extension Cloud Model

In order to scientifically and reasonably assess the risk of water inrush from the coal seam floor, considering the influence of natural environmental factors such as hydrogeology, mining, and human intervention, the PSR model of ecosystem health evaluation was introduced, and the risk evaluation indicator system of water inrush from the coal seam floor was established. In order to solve the randomness and fuzziness of water inrush event evaluation, the evaluation model is constructed based on extension cloud theory and is applied in the 12123 working face of Pan Er coal mine of Huainan Mining Group. The application results show that the evaluation results are basically consistent with the actual situation, which shows that the model can be used in the actual evaluation work and is scientific.
The Content of Tolerance Education in Short Story Learning in High Schools

Short stories have the advantage of teaching materials. Because of its short form, it is flexible to be used as teaching materials that do not take long to read. This study aims to describe the teachers’ perception of the urgency of integrating tolerance education in the short story appreciation, the content of tolerance values in the short story appreciation learning instruments in high school, and the content of tolerance values in the short story appreciation teaching materials in textbooks. In order to achieve that, a descriptive qualitative method was employed in this study. The results are as follows. (1) All Indonesian teachers in senior high schools in Surakarta have the perception that the value of tolerance education is important to be integrated into learning, including in the short story appreciation learning. (2) The character of tolerance is always listed in the Syllabus document and the Core Competency component of the lesson plan. Apart from that component, the character of tolerance is implicitly included in the character of "caring" and "open to others' opinions" in 86% of the syllabus and lesson plans. (3) There are 9% of short story appreciation teaching materials that have tolerance education content. The potential for this tolerance content is found in a lot of teaching material topics in the form of learning activities and short stories.&#x0D; &#x0D; Keywords: Character Education, Short story appreciation, Teaching material, Textbooks
Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm. However, it suffers search stagnation from being trapped into a sub-optimal solution in an optimization problem. This paper proposes a novel hybrid algorithm (SDPSO) to improve its performance on local searches. The algorithm merges two strategies, the static exploitation (SE, a velocity updating strategy considering inertia-free velocity), and the direction search (DS) of Rosenbrock method, into the original PSO. With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search. The SDPSO algorithm was implemented and tested on unconstrained benchmark problems (CEC2014) and some constrained engineering design problems. The performance of SDPSO is compared with that of other optimization algorithms, and the results show that SDPSO has a competitive performance.
Flight planning in multi-unmanned aerial vehicle systems: Nonconvex polygon area decomposition and trajectory assignment

Nowadays, it is quite common to have one unmanned aerial vehicle (UAV) working on a task but having a team of UAVs is still rare. One of the problems that prevent us from using teams of UAVs more frequently is flight planning. In this work, we present the first open-source solution ( https://pypi.org/project/pode/ ) for splitting any complex area into multiple parts. The area of interest can be convex or nonconvex and can include any number of no-flight zones. Four solutions, based on the algorithm of Hert and Lumelsky, are tested with the aim of improving the compactness of the partitions. We also show how the shape of the partitions influences flight performance in a real case scenario.
Constructing squeezed states of light with associated Hermite polynomials

A new class of states of light is introduced that is complementary to the well-known squeezed states. The construction is based on the general solution of the three-term recurrence relation that arises from the saturation of the Schr\"odinger inequality for the quadratures of a single-mode quantized electromagnetic field. The new squeezed states are found to be linear superpositions of the photon-number states whose coefficients are determined by the associated Hermite polynomials. These results do not seem to have been noticed before in the literature. As an example, the new class of squeezed states includes superpositions characterized by odd-photon number states only, so they represent the counterpart of the prototypical squeezed-vacuum state which consists entirely of even-photon number states.
Economic Aspects of Emergency Transition to Distance Education, or The Price of Going Online in Higher Education

As Russian universities switched to distance education in March 2020 to prevent COVID-19 from spreading, self-paying students started questioning the fairness of tuition fees during the pandemic. They filed petitions, emphasizing that distance learning could not be equated to traditional classroom-based learning, that educational services were not delivered to the full extent, and that educational quality had decreased. On those grounds, students required cutting tuition fees down to the size of those in part-time or extramural education. To understand whether universities can afford making this step, we undertake to measure the price that they have paid for the transition to distance learning.For this purpose, we use data from a survey of faculty teaching and curriculum organization practices carried out at a federal university between March 23, 2020 and June21, 2020, which involved 4,099 faculty members, as well as financial records of some departments within that university. Findings show that teaching workload reduced by 15% with distance learning during the pandemic, and the number of contact hours decreased 1.7 times. However, the overall amount of faculty workload increased by 50%, first of all due to a 2.4-time rise in curriculum organization activities. Therefore, the transition to distance education led to a significant increase in faculty workload, given that contact hours were preserved. Furthermore, the university invested heavily in the transition to distance learning and continuity of educational processes during the pandemic, in particular by financing the establishment of a new department for digitalization of learning processes.An inference is made that distance education imposed by the pandemic has not been reduced to part-time or extramural studies. Decisions about cutting tuition fees for self-paying students should be made at the institutional level, with due regard for faculty workload and digitization costs.
Fault-Tolerant Logical Gates in Holographic Stabilizer Codes Are Severely Restricted

The future of holographic stabilizer codes for achieving universal fault-tolerant quantum computation does not appear promising: severe limitations on its usefulness are conceptually presented and theoretically verified.