Reviewerly/exHarmony huggingface dataset:
Implementing a multichain framework using hyperledger for supply chain transparency in a dynamic partnership: A feasibility study

As an emerging technology, blockchain is appealing because it ensures authenticity and legitimacy of business activities, improves traceability of products, and enhances supply chain transparency (SCT), thus strengthening trust and confidence among partners. Although academicians and practitioners agree that information sharing is imperative to improve SCT, it is unclear whether and how organizations can take advantage of blockchain technology to manage information sharing when collaborating with partners and competitors in a dynamic supply chain while maintaining privacy and confidentiality. In this study, we attempt to address this issue by building multiple assets and chains with multiple processes, facilitating data aggregation, and maintaining information confidentiality. To achieve these objectives, this study proposes a multi-asset, multichain framework and builds a permissioned blockchain network using Hyperledger Fabric. By constructing "channels" under the proposed framework, this study demonstrates that firms can share information privately and confidentially with partners and competitors in a supply chain and simultaneously improve SCT. Overall, the results obtained from the proof-of-concept analysis suggest that the proposed framework outperforms the public blockchain in terms of information sharing and SCT perspectives.
Improved Gravitational Search Algorithm Based on Adaptive Strategies

The gravitational search algorithm is a global optimization algorithm that has the advantages of a swarm intelligence algorithm. Compared with traditional algorithms, the performance in terms of global search and convergence is relatively good, but the solution is not always accurate, and the algorithm has difficulty jumping out of locally optimal solutions. In view of these shortcomings, an improved gravitational search algorithm based on an adaptive strategy is proposed. The algorithm uses the adaptive strategy to improve the updating methods for the distance between particles, gravitational constant, and position in the gravitational search model. This strengthens the information interaction between particles in the group and improves the exploration and exploitation capacity of the algorithm. In this paper, 13 classical single-peak and multi-peak test functions were selected for simulation performance tests, and the CEC2017 benchmark function was used for a comparison test. The test results show that the improved gravitational search algorithm can address the tendency of the original algorithm to fall into local extrema and significantly improve both the solution accuracy and the ability to find the globally optimal solution.
Mobile-learning adoption in teacher education amidst COVID-19: Identifying two critical stages by exploring teachers’ emotions

Mobile learning (ML) is extremely relevant to distance teaching. Although much is known about ML usage in teacher education, less is known about crucial points in teachers’ ML adoption process under constraints such as the COVID-19 pandemic. The aim of this exploratory case study was to gain insight into the ML adoption process, including its critical points, by examining teachers’ emotion-related language. This study investigated the emotional response of 32 inservice teachers to Mobile Learning (ML) adoption while attending ML training during the COVID-19 pandemic. The data were collected using semi-structured interviews (10), focus groups (3), and participants’ reflections (96) at five time points. The data underwent multilevel analysis (content and linguistic analyses), revealing two critical stages during the ML adoption process and indicating several factors that may affect the quality of emotional response, thereby promoting or impeding this process. The study highlights the critical sages and their related features that must be addressed to promote optimal ML adoption in teacher education in both routine and emergency conditions.
Autonomous driving using imitation learning with look ahead point for semi structured environments

Semi-structured environments are difficult for autonomous driving because there are numerous unknown obstacles in drivable area without lanes, and its width and curvature considerably change. In such environments, searching for a path on a real-time is difficult, and localization data are inaccurate, reducing path tracking accuracy. Instead, alternative methods that reactively avoid obstacles in real-time using candidate paths or an artificial potential field have been studied. However, these require heuristics to identify specific parameters for handling various environments and are vulnerable to inaccurate input data. To address these limitations, this study proposes a method in which a vehicle drives toward drivable area using vision and deep learning. The proposed imitation learning method learns the look-ahead point where the vehicle should reach on a vision-based occupancy grid map to obtain a safe policy with a clear state action pattern relationship. Furthermore, using this point, the data aggregation (DAgger) algorithm with weighted loss function is proposed, which imitates expert behavior more accurately, especially in unsafe or near-collision situations. Experimental results in actual semi-structured environments demonstrated the limitations of general model-based methods and the effectiveness of the proposed imitation learning method. Moreover, simulation experiments showed that DAgger with the weight obtains a safer policy than existing DAgger algorithms.
Plasma image classification using cosine similarity constrained convolutional neural network

Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei, and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in investigating not only the underlying physics of the experiments but the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction, and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93% accurate binary classification to distinguish unstable columns from stable columns and 92% accurate five-way classification of a small, labeled data set which includes three classes corresponding to varying levels of kink instability.
Proof-of-Principle Direct Measurement of Particle Statistical Phase

The symmetrization postulate in quantum mechanics is formally reflected in the appearance of an exchange phase ruling the symmetry of identical particle global states under particle swapping. Many indirect measurements of this fundamental phase have been reported so far, while a direct observation has been only recently achieved for photons. Here we propose a general scheme capable to directly measure the exchange phase of any type of particles (bosons, fermions, anyons), exploiting the operational framework of spatially localized operations and classical communication. We experimentally implement it in an all-optical platform providing proof-of-principle for different simulated exchange phases. As a byproduct, we supply a direct measurement of the real bosonic exchange phase of photons. Also, we analyze the performance of the proposed scheme when mixtures of particles of different nature are injected. Our results confirm the symmetrization tenet and provide a tool to explore it in various scenarios.
Soft Masking for Cost-Constrained Channel Pruning

Structured channel pruning has been shown to significantly accelerate inference time for convolution neural networks (CNNs) on modern hardware, with a relatively minor loss of network accuracy. Recent works permanently zero these channels during training, which we observe to significantly hamper final accuracy, particularly as the fraction of the network being pruned increases. We propose Soft Masking for cost-constrained Channel Pruning (SMCP) to allow pruned channels to adaptively return to the network while simultaneously pruning towards a target cost constraint. By adding a soft mask re-parameterization of the weights and channel pruning from the perspective of removing input channels, we allow gradient updates to previously pruned channels and the opportunity for the channels to later return to the network. We then formulate input channel pruning as a global resource allocation problem. Our method outperforms prior works on both the ImageNet classification and PASCAL VOC detection datasets.
Semi-supervised Single-View 3D Reconstruction via Prototype Shape Priors

The performance of existing single-view 3D reconstruction methods heavily relies on large-scale 3D annotations. However, such annotations are tedious and expensive to collect. Semi-supervised learning serves as an alternative way to mitigate the need for manual labels, but remains unexplored in 3D reconstruction. Inspired by the recent success of semi-supervised image classification tasks, we propose SSP3D, a semi-supervised framework for 3D reconstruction. In particular, we introduce an attention-guided prototype shape prior module for guiding realistic object reconstruction. We further introduce a discriminator-guided module to incentivize better shape generation, as well as a regularizer to tolerate noisy training samples. On the ShapeNet benchmark, the proposed approach outperforms previous supervised methods by clear margins under various labeling ratios, (i.e., 1%, 5% , 10% and 20%). Moreover, our approach also performs well when transferring to real-world Pix3D datasets under labeling ratios of 10%. We also demonstrate our method could transfer to novel categories with few novel supervised data. Experiments on the popular ShapeNet dataset show that our method outperforms the zero-shot baseline by over 12% and we also perform rigorous ablations and analysis to validate our approach. Code is available at https://github.com/ChenHsing/SSP3D .
Detection of Colorectal Cancer with Vision Transformers

Colorectal cancer (CRC) is the third most prevalent cancer in the U.S. Aside from lung cancer, it is the second leading cause of death among all kinds of cancers. According to the American Cancer Society (ACS), there are expected to be 106.180 new cases of colon cancer and 4,850 new cases of rectal cancer in the United States by 2022. The histological categorization of CRC tissue is critical to diagnosing the disease and making treatment decisions. However, it is challenging to classify CRC histological images due to the wide variety of tissue patterns. To alleviate this issue, we introduce a CRC detection methodology based on vision transformers. The performance verification of the CRC detection methodology is performed on a well-known benchmark, namely CRC-5000. According to the results, our methodology achieves significantly better diagnosis performance than a variety of recent works, one of which is also wrapped around vision transformers.
Photo-realistic Neural Domain Randomization

Synthetic data is a scalable alternative to manual supervision, but it requires overcoming the sim-to-real domain gap. This discrepancy between virtual and real worlds is addressed by two seemingly opposed approaches: improving the realism of simulation or foregoing realism entirely via domain randomization. In this paper, we show that the recent progress in neural rendering enables a new unified approach we call Photo-realistic Neural Domain Randomization (PNDR). We propose to learn a composition of neural networks that acts as a physics-based ray tracer generating high-quality renderings from scene geometry alone. Our approach is modular, composed of different neural networks for materials, lighting, and rendering, thus enabling randomization of different key image generation components in a differentiable pipeline. Once trained, our method can be combined with other methods and used to generate photo-realistic image augmentations online and significantly more efficiently than via traditional ray-tracing. We demonstrate the usefulness of PNDR through two downstream tasks: 6D object detection and monocular depth estimation. Our experiments show that training with PNDR enables generalization to novel scenes and significantly outperforms the state of the art in terms of real-world transfer.
Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information

Non-negative matrix factorization (NMF) based topic modeling is widely used in natural language processing (NLP) to uncover hidden topics of short text documents. Usually, training a high-quality topic model requires large amount of textual data. In many real-world scenarios, customer textual data should be private and sensitive, precluding uploading to data centers. This paper proposes a Federated NMF (FedNMF) framework, which allows multiple clients to collaboratively train a high-quality NMF based topic model with locally stored data. However, standard federated learning will significantly undermine the performance of topic models in downstream tasks (e.g., text classification) when the data distribution over clients is heterogeneous. To alleviate this issue, we further propose FedNMF+MI, which simultaneously maximizes the mutual information (MI) between the count features of local texts and their topic weight vectors to mitigate the performance degradation. Experimental results show that our FedNMF+MI methods outperform Federated Latent Dirichlet Allocation (FedLDA) and the FedNMF without MI methods for short texts by a significant margin on both coherence score and classification F1 score.
Threat Actors’ Tenacity to Disrupt: Examination of Major Cybersecurity Incidents

The exponential growth in the interconnectedness of people and devices, as well as the upward trend in cyberspace usage will continue to lead to a greater reliance on the internet. Most people’s daily activities are dependent on their ability to navigate the internet to access and manage information. There are usually real risks associated with managing or accessing information, and these risks when exploited by threat actors, often lead to cybersecurity incidents. It is a common knowledge that a major cybersecurity incident is likely to result in significant financial losses, legal liability, privacy violations, reputational damage, sensitive data compromises, as well as national security implications. Threat actors usually employ various attack techniques to cause these incidents. After we identified the major cybersecurity incident report that is consolidated by the Center for Strategic & International Studies from which we derived the data about the 803 major incidents that we analyzed, we then verified its credibility, non-partisan, global outreach and cybersecurity attack coverage by cross-referencing it with Data Breach Investigation Report (DBIR). We also through the lens of the Global Cybersecurity Index (GCI) ensured that this study is conducted within the context of cybersecurity principles. In reference to these attack techniques employed by threat actors, we conducted an exploratory investigation of 803 major cybersecurity incidents that were reported over the last decade. From a group of 244 of these major security incidents that happened and were reported between 2005 and 2021, this study reports that malware attack techniques were employed by threat actors to cause 48 percent of them and phishing attack techniques account for 19.7 percent of them. As many sources have confirmed the fact that major incidents will always happen, we highlighted the importance of readiness of organizations to conduct cybersecurity incident triage and or thorough investigation as necessary. Given the relevance of the guidelines outlined in the National Institute of Standards and Technology (NIST) incident response framework, we recommend that organizations should adopt it or similar guidelines as best as possible.
Generation of Truly Random Numbers on a Quantum Annealer

This study investigates how qubits of modern quantum annealers (QA) such as D-Wave can be applied for generating truly random numbers. We show how a QA can be initialised and how the annealing schedule can be set so that after the annealing, thousands of truly random binary numbers are measured in parallel. Those can then be converted to uniformly distributed natural or real numbers in desired ranges, either biased or unbiased. We discuss the observed qubits’ properties and their influence on the random number generation and consider various physical factors that influence the performance of our generator, i.e., digital-to-analogue quantisation errors, flux errors, temperature errors and spin bath polarisation. The numbers generated by the proposed algorithm successfully pass various tests on randomness from the NIST test suite. Our source code and large sets of truly random numbers are publicly available on our project web page <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://4dqv.mpi-inf.mpg.de/QRNG/</uri></monospace> .
Development of forms of interaction between the enterprise and the university within the framework of the dual education model: experience and prospects

Introduction. One of the most important tasks of the modern education system in Russia is the search for organizational forms that can meet the needs of the labor market, solve the problem of providing enterprises, especially high-tech ones, with personnel with deep fundamental knowledge and relevant practical skills. The answer to this request is the development of a dual education system that takes into account the modern realities of socio-economic development. The purpose of the work is to study the development of forms of organization of interaction between enterprises and educational organizations within the framework of dual education. Materials and methods. In order to study the significance and prospects for the development of the dual education system, a retrospective and comparative analysis, an ecosystem approach were used. The study involved 98 graduates of one of the basic departments of the Don State Technical University (Russian Federation), who studied from 2003 to 2020 and are currently working at a partner enterprise. For the purpose of comparative analysis and confirmation of the hypothesis about the impact of training at the basic department on the acceleration of professional adaptation, two groups of employees (35 people each) with work experience from three to ten years were also interviewed: those who studied at this department and those who did not study at it. The method of group expert assessments, questionnaires, the Mann-Whitney U-test are applied Results. A survey of employees of a large Russian industrial enterprise who studied at the basic department and did not study at it showed that the average period of professional adaptation of the first was about 4.9 months, which is more than twice less than the same period in the group of those who did not study at it. Similarly, the period of perception of the corporate culture of the enterprise: for those who studied at the basic department, it is more than two times shorter than for those who did not study at it. Both groups of respondents rated the importance of mentoring for young workers by 5 points on a 5-point scale. The Mann-Whitney U-criterion for assessing the period of professional adaptation is 108.5, for assessing the perception of corporate culture – 86.5 (at U0.01=413; U0.05=471). Conclusion. The results of the study allow us to conclude about the significance of the accumulated experience in the development of the domestic dual education system and its key institutional unit – the basic department at the partner enterprise. The prospects for the development of dual education are seen in the dissemination of the principle of lifelong learning. This principle presupposes the formation of a new model of dual education as part of a vocational education ecosystem that allows taking into account current and prospective personnel requirements of enterprises.
CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking

How to combine the complementary capabilities of an ensemble of different algorithms has been of central interest in visual object tracking. A significant progress on such a problem has been achieved, but considering short-term tracking scenarios. Instead, long-term tracking settings have been substantially ignored by the solutions. In this paper, we explicitly consider long-term tracking scenarios and provide a framework, named CoCoLoT, that combines the characteristics of complementary visual trackers to achieve enhanced long-term tracking performance. CoCoLoT perceives whether the trackers are following the target object through an online learned deep verification model, and accordingly activates a decision policy which selects the best performing tracker as well as it corrects the performance of the failing one. The proposed methodology is evaluated extensively and the comparison with several other solutions reveals that it competes favourably with the state-of-the-art on the most popular long-term visual tracking benchmarks.
Design Principles of Secure Certificateless Signature and Aggregate Signature Schemes for IoT Environments

Certificateless cryptography resolves the certificate management problem of public-key cryptography and the key-escrow problem of identity-based cryptography. An aggregate signature scheme which allows to aggregate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> distinct signatures on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> distinct messages of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> distinct signers into a single signature reduces communication overhead and computational cost. Due to the suitability of certificateless signature (CLS) and certificateless aggregate signature (CLAS) schemes for IoT environments, similar CLS and CLAS schemes have been proposed for a long time and, despite their security proofs, they have been attacked and modified to prevent the attacks. Even now, similar design methods and similar attacks on the schemes are being repeated. In order to prevent the similar attacks on the schemes, it is necessary to analyze their causes and vulnerabilities. In this paper, we first show that recently proposed five CLS and CLAS schemes are insecure against universal forgery attacks, type I attacks, type II attacks or malicious-but-passive-KGC attacks. We discuss their security flaws, causes and countermeasures. We then present design principles to prevent various algebraic attacks including our attacks. The design principles will help in the construction of secure CLS and CLAS schemes against the previous attacks and potential attacks.