Reviewerly/exHarmony huggingface dataset:
Probabilistic quantum teleportation of shared quantum secret

Very recently, Lee et al. proposed a secure quantum teleportation protocol to transfer shared quantum secret between multiple parties in a network [ Phys. Rev. Lett. 124 060501 (2020)]. This quantum network is encoded with a maximally entangled GHZ state. In this paper, we consider a partially entangled GHZ state as the entanglement channel, where it can achieve, probabilistically, unity fidelity transfer of the state. Two kinds of strategies are given. One arises when an auxiliary particle is introduced and a general evolution at any receiver’s location is then adopted. The other one involves performing a single generalized Bell-state measurement at the location of any sender. This could allow the receivers to recover the transmitted state with a certain probability, in which only the local Pauli operators are performed, instead of introducing an auxiliary particle. In addition, the successful probability is provided, which is determined by the degree of entanglement of the partially multipartite entangled state. Moreover, the proposed protocol is robust against the bit and phase flip noise.
Development of Artificial Intelligence Campus and Higher Education Management System Under the Background of Big Data and WSN

This article uses "big data" to upgrade traditional majors, promote the co-construction and sharing of digital resources, excellent teachers, and educational data, enhance the information literacy of teachers and students, build smart classrooms and virtual factories, widely use online and offline mixed teaching, and promote the overall trend of informationization. "Smart Campus" is moving forward and is divided into four functional modules: student curriculum management module, information release and communication module, teaching support module, and daily office management module. The higher education management system uses the internationally popular WSN technology.
The “Bitcoin Generator” Scam

The “Bitcoin Generator Scam” (BGS) is a cyberattack in which scammers promise to provide victims with free cryptocurrencies in exchange for a small mining fee. In this paper, we present a data-driven system to detect, track, and analyze the BGS. It works as follows: we first formulate search queries related to BGS and use search engines to find potential instances of the scam. We then use a crawler to access these pages and a classifier to differentiate actual scam instances from benign pages. Last, we automatically monitor the BGS instances to extract the cryptocurrency addresses used in the scam. A unique feature of our system is that it proactively searches for and detects the scam pages. Thus, we can find addresses that have not yet received any transactions. Our data collection project spanned 16 months, from November 2019 to February 2021. We uncovered more than 8,000 cryptocurrency addresses directly associated with the scam, hosted on over 1,000 domains. Overall, these addresses have received around 8.7 million USD, with an average of 49.24 USD per transaction. Over 70% of the active addresses that we are capturing are detected before they receive any transactions, that is, before anyone is victimized. We also present some post-processing analysis of the dataset that we have captured to aggregate attacks that can be reasonably confidently linked to the same attacker or group. Our system is one of the first academic feeds to the APWG eCrime Exchange database. It has been actively and automatically feeding the database since November 2020.
Augmented Reality Fitts' Law Input Comparison Between Touchpad, Pointing Gesture, and Raycast

With the goal of exploring the impact of transparency on selection in augmented reality (AR), we present a Fitts&#x0027; law experiment with 18 participants, comparing three different input methods (finger based Pointing Gesture, controller using the Touchpad, and controller using Raycast), across 4 different target transparency levels (0&#x0025;, 30&#x0025;, 60&#x0025;, and 90&#x0025;) in an optical see-through AR head-mounted display. The results indicate that transparency has little effect on selection throughput and error rates. Overall, the Raycast input method performed significantly better than the pointing gesture and Touchpad inputs in terms of error rate and throughput in all opacity conditions.
Semi-Automatic Ontology Matching Based on Interactive Compact Genetic Algorithm

Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automatic ontology matching techniques suffer from the limitation of similarity measure, whose alignment’s quality cannot be ensured. To overcome this drawback, in this work, an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique is proposed, which utilizes both the compact encoding mechanism and expert interacting mechanism to improve the algorithm’s performance and the alignment’s quality. In addition, an optimization model is established to formally define the ontology entity matching problem, and an efficient interacting strategy is proposed, which is able to reduce the expert’s workload and maximize his working value. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test our proposal’s performance. The experimental results show that our approach is able to make use of the expert knowledge to improve the alignment’s quality, and it also outperforms OAEI’s participants.
Lattice-based provable data possession in the standard model for cloud-based smart grid data management systems

The smart grid is considered to be the next-generation electric power network. In a smart grid, there are massive data to be processed, so cloud computing is introduced into it to form a cloud-based smart grid data management system. However, with data no longer being stored locally, how to ensure the integrity of data stored in the cloud in the smart grid has become an urgent problem awaiting solution. Provable data possession has been proposed to solve this problem. With the development of quantum computer technology, quantum attacks-resistant cryptographic schemes are gradually entering people’s horizons. Lattice cryptography can resist quantum attacks. In this article, a lattice-based provable data possession scheme is proposed for cloud-based smart grid data management systems. The scheme is proved unforgeable under the small integer solution hard assumption in the standard model. Compared with other two efficient lattice-based provable data possession schemes in the standard model, our scheme also shows efficiency.
Rapid Exploration Using pXRF Combined with Geological Connotation Method (GCM): A Case Study of the Nuocang Cu Polymetallic District, Tibet

The Nuocang Cu polymetallic district is located in western Gangdese, close to the south of the Luobadui–Milashan fault zone. A large number of metal deposits with the potential to be prospected, such as Chagele, Zhalong, Longgen, and Sangmola, have been found near this district. To further isolate the target prospecting area, we used a portable X-ray fluorescence spectrometer (pXRF) to conduct an in situ 1:10,000 soil pedogeochemical survey. The results show that the use of a pXRF combined with the geological connotation method (GCM) can accurately delineate the anomalies related to mineralization and highlight “weak” and “small” anomalies. It was also shown to effectively shorten the working cycle and ensure the continuity and timeliness of field work. Through sizing tests, the analysis of −10~+60 mesh soil samples achieved the best anomaly delineation effect. By studying the supra-ore, near-ore, sub-ore halo, and Th/U, the degree of denudation and the oxidation-reduction environment of the deposit were judged to be moderate. Ultimately, depending on the target area delineated by the pXRF, six Cu-Pb-Zn orebodies were uncapped by five exploratory trenches, which proved the potentiality of the Cu-Pb-Zn polymetallic epithermal deposit controlled by minor faults in Central Nuocang.
Generative Session-based Recommendation

Session-based recommendation has recently attracted increasing attention from both industry and academic communities. Previous models mostly focus on designing different models to fit the observed data, which can be quite sparse in real-world scenarios. To alleviate this problem, in this paper, we propose a novel generative session-based recommendation framework. The main building block of our idea is to develop a generator to simulate user sequential behaviors, which are leveraged to train and improve the target sequential recommender model. In order to generate high quality samples, we consider two aspects: (1) the rationality as a sequence of user behaviors, and (2) the informativeness for training the target model. To satisfy these requirements, we design a doubly adversarial network. The first adversarial module aims to make the generated samples conform to the underlying patterns of the real user sequential preference (rationality requirement). The second adversarial module is targeted at widening the model experiences by generating samples which can induce larger model losses (informativeness requirement). In our model, the samples are generated based on a reinforcement learning strategy, where the reward is related with both of the above aspects. In order to stable the training process, we introduce a self-paced regularizer to learn the agent in an easy-to-hard manner. We conduct extensive experiments based on real-world datasets to demonstrate the effectiveness of our model.
AVPL: Augmented visual perception learning for person Re-identification and beyond

In this work, we propose an Augmented Visual Perception Learning (AVPL) method for Person Re-identification (ReID) which is inspired by the two-stream hypothesis theory of Human Visual System (HVS). Deep learning methods dominate ReID and many state-of-the-art performances are achieved from the perspective of optimizing the model of 'what' visual pathway. It does not blend 'what' and 'where' well. Our AVPL method uses the essential mechanism of the ventro-dorsal stream of the 'where' visual pathway to expand the perception field of the model, and integrates with the 'what' to complete the information of the visually salient regions. A novel Batch Attention (BA), the key component of our Augmented Visual Perception (AVP) module, is proposed to apply fusion and augmentation into all input feature maps within each batch. Through AVP module, the improved attention-based model attaches more importance to enhancement of salient features, therefore acquiring better perceptual ability of salient regions which provide the most distinguishably distinctions for ReID. Extensive experiments have been carried out on four main stream ReID datasets and two recognition datasets. In terms of ReID, our method achieves rank-1 accuracy of 95.2% on CUHK03-NP, 98.7% on Market-1501, 96.0% on DukeMTMC-reID and 88.8% on MSMT17-V1, outperforming the state-of-the-art methods by a large margin. Besides, it has been experimentally proven to be applicable and effective in other recognition tasks including facial expression recognition and action recognition with an improved accuracy.
Component‐based nearest neighbour subspace clustering

In this paper, the problem of clustering data points that lie near or on a union of independent low-dimensional subspaces is addressed. To this end, the popular spectral clustering-based algorithms usually follow a two-stage strategy that initially builds an affinity matrix and then applies spectral clustering. However, an inappropriate affinity matrix that does not sufficiently connect data points lying on the same subspace will easily lead to the issue of over-segmentation. To alleviate this issue, building the affinity matrix based on subspace hypotheses generated by an iterative sampling operation according to the Random Cluster Model under the framework of energy minimisation is proposed. Specifically, each hypothesis is generated from a large number of data points by sampling a component in a K-nearest neighbour graph. Extensive experiments on synthetic data and real-world datasets show that the proposed method can improve the connectivity of the affinity matrix and provide competitive results against state-of-the-art methods.
Considerations for Building Solidarity among Academic and Tech Workers

This panel reflects on the conditions of collaboration as well as the possibilities of solidarity between academic and tech workers by drawing on the experiences of panelists who have pondered questions of ethics, responsibility and values in technology-building from a range of positionalities within, adjacent to and outside of academic and technology organizations. As the proposal outlines, the panel will think through opportunities as well as risks for academic and tech workers to work towards progressive tech futures together but also the differences and impossibilities that arise with each position.
A Novel Steganography-Based Pattern for Print Matter Anti-Counterfeiting by Smartphone Cameras

Print matter authentication based on anti-counterfeiting techniques has received continuously increasing concern from academia and industry. However, the existing printing anti-counterfeiting solutions often have the defects of poor identification experience, high cost, or weak anti-counterfeiting ability, and cannot achieve pre-sale anti-counterfeiting. Therefore, a novel steganography-based pattern for print matter anti-counterfeiting by smartphone cameras is proposed in this study. Firstly, every pixel in the original binary message image (such as QR code) is replaced by a square pixel block with the same binary gray value of 0 or 255 (the first-level expansion). Secondly, the obtained image is encrypted based on the logistic chaotic sequence, and then scrambled by Arnold transform. Lastly, once again every pixel in the generated image is replaced with a square pixel block (the second-level expansion), the size and gray value of which can be set to control the semi-fragile ability to distinguish an originally printed pattern from its illegitimate copy. If the message extracted from the printed pattern through the inverse procedure is complete enough to decode and read, the pattern is assumed to be an original print. Experimental results verify the advancement and effectiveness of the proposed scheme in distinguishing the copied pattern.
A Short Text Clustering Approaches in Social Media

In general, handling bag-of-words representation or TF-IDF is an arduous task with short text clustering as it results in sparse vector representation mainly for short texts. This paper provides an in-depth study on learning predictor features through an auto encoder model and sentence embedding technique. Assignment of user from any clustering technique as a process of supervision in order to update the weight of the encoder analysis. Short text related datasets validate of measure effective methods and algorithms for short text unsupervised data. Among this short text are not able to clustering in some social media. This paper discusses the challenge and methods to cluster in social media short text challenging issues. Clustering methods of algorithms to sort the problem to recovery the short text using K-means algorithms of Convolution Neural Network method. This paper provides a comprehensive review of short text prediction using the clustering algorithm, explore the research challenges, and open issues in this area.
K. D. Ushinsky’s date of birth: completion of discussion

The problem associated with determining the exact date of birth of great Russian teacher Konstantin Dmitrievich Ushinsky is being considered. There is a contradiction in the available documentary sources, which became the basis for the appearance of different versions, there are two main ones for 2021 — February 19, 1823 and February 19, 1824. It is noted that the basis of the version of 1824 was the memoirs of Ushinsky himself, his close friends M. K. Chaly and Yu. S. Rekhnevsky, as well as his first biographers. The unreliability of memories as a historical source is emphasized, in support of the memoirs of Chaly, from which one can conclude both about 1823 and about 1824. The reasoning of the Soviet adherents of version 1824 is considered, based on the authors' assumptions about deception by the parents of K. D. Ushinsky of the Tula Spiritual Consistory to obtain a birth certificate, making son Konstantin a year elder than the true age, with the aim of earlier enrollment in the gymnasium. Documents of the State Archive of the Tula Region related to the issue of identifying the Tula Spiritual Consistory of witness and documentary evidence of the birth and baptising Konstantin on February 19, 1823 in Tula are analyzed. It is emphasized that the testimony of witnesses is supported by irrefutable documentary evidence — a formal list for the service of Father D. G. Ushinsky, issued by the office of the Minister of Finance of the Russian Empire. The role of ascetics — teachers of Yaroslavl State Pedagogical University named after K. D. Ushinsky and Tula State Pedagogical University named after L. N. Tolstoy in substantiating and promoting the version of 1823 is considered. The decisive role of the President of the Russian Federation V. V. Putin in the final recognition of 1823 the year of K. D. Ushinsky birth is emphasized.
Assessing Usability of Untethered Head-Mounted Displays for Medical Education

Introduction Recent meta-analyses have found immersive technology to be effective for training, yet there is limited research on user experience with head-mounted displays (HMDs) in the medical domain. If emerging immersive displays do not meet usability standards in the context of healthcare simulation, the technology may cause frustration or hinder learning outcomes. This is the first experimental comparison of usability in commercial untethered, “all-in-one” HMDs for healthcare simulation. Methods The usability and comfort of three commercial untethered HMDs (Oculus Go, Oculus Quest, and Lenovo Mirage Solo) were tested using a randomized within-person design such that each headset was evaluated by all participants in a random sequence. During the experiment, participants (n = 9) interacted with a simulated healthcare environment in each headset and then responded to usability and comfort surveys. Results All of the HMDs were rated as having higher than average usability compared with an industry benchmark scale, the System Usability Scale. Only one of the headsets had a usability rating in the highest range, which was significantly higher than the lowest rated headset ( P = 0.047, Cohen d = 0.901). In addition, feelings of discomfort with the headsets were low, and comfort ratings did not differ significantly between headsets ( P &gt; 0.05). Conclusions Untethered HMDs had acceptable user experience ratings during a healthcare simulation task, but some headsets were rated higher on usability. Because usability is important for learner engagement and training outcomes, educators should confirm that immersive displays meet usability standards before implementation.
WIDAR - Weighted Input Document Augmented ROUGE

The task of automatic text summarization has gained a lot of traction due to the recent advancements in machine learning techniques. However, evaluating the quality of a generated summary remains to be an open problem. The literature has widely adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as the standard evaluation metric for summarization. However, ROUGE has some long-established limitations; a major one being its dependence on the availability of good quality reference summary. In this work, we propose the metric WIDAR which in addition to utilizing the reference summary uses also the input document in order to evaluate the quality of the generated summary. The proposed metric is versatile, since it is designed to adapt the evaluation score according to the quality of the reference summary. The proposed metric correlates better than ROUGE by 26%, 76%, 82%, and 15%, respectively, in coherence, consistency, fluency, and relevance on human judgement scores provided in the SummEval dataset. The proposed metric is able to obtain comparable results with other state-of-the-art metrics while requiring a relatively short computational time (Implementation for WIDAR can be found at - https://github.com/ Raghav10j/WIDAR ).
Tracking Tangible Asset Ownership and Provenance with Blockchain

ABSTRACT Blockchain transactions are recorded in a shared and append-only repository that multiple parties verify, validate, and agree upon. While initially used to keep track of digital assets, blockchains now track the ownership and provenance of tangible assets. An inherent challenge in using blockchain for this task involves keeping the status of a tangible asset in the physical world in sync with its non-fungible token on a blockchain. While several blockchains are already being used in this manner, specific implementation details are fragmented. In response, this study examines four stages of tracking tangible assets using a consortium's permissioned blockchain, including: design and governance of a blockchain, asset creation, asset transfer, and asset retirement. Based on this analysis, this study proposes a framework of risk considerations and control objectives to evaluate the extent to which a unique blockchain serves as a reliable transaction repository for tracking the ownership and provenance of tangible assets.
SD<sup>2</sup>: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance

Comprehensively evaluating and comparing researchers’ academic performance is complicated due to the intrinsic complexity of scholarly data. Different scholarly evaluation tasks often require the publication and citation data to be investigated in various manners. In this article, we present an interactive visualization framework, SD <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , to enable flexible data partition and composition to support various analysis requirements within a single system. SD <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> features the hierarchical histogram, a novel visual representation for flexibly slicing and dicing the data, allowing different aspects of scholarly performance to be studied and compared. We also leverage the state-of-the-art set visualization technique to select individual researchers or combine multiple scholars for comprehensive visual comparison. We conduct multiple rounds of expert evaluation to study the effectiveness and usability of SD <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> and revise the design and system implementation accordingly. The effectiveness of SD <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> is demonstrated via multiple usage scenarios with each aiming to answer a specific, commonly raised question.