Reviewerly/exHarmony huggingface dataset:
Evaluation of Times and Best Solutions of MFO, LSA and PSO Using Parallel Computing, Fuzzy Logic Systems and Migration Blocks Together to Evaluate Benchmark Functions

In this chapter, we propose the creation of a development environment in which we will work with each of the optimization algorithms using parallel computing in order to apply the use of migration blocks that allow us to share information between each of the populations of the algorithms, as well as the use the Fuzzy logic systems that allows us to adjust the migration percentage that we will use and the number of times or how often the migration will be applied. The algorithms that we going to use are the following: Moth-Flame Optimization, Lightning Search Algorithm and Particle Swarm Optimization, where on the other hand the use of Fuzzy logic will be applied internally in the process each of the algorithms, which allows us the dynamic adjustment of parameters of each algorithm separately. The algorithms used will be evaluated by the results obtained as well as the individual or total duration of each benchmark function that we are using.
Quartet: A Logarithmic Size Linkable Ring Signature Scheme from DualRing

Ring signature (RS) allows a user to sign a message on behalf of a group without exposing who the true signer is. In addition, linkable ring signature (LRS) allows signatures can be publicly verified whether they were generated by the same singer. In Crypto 2021, Yuen et al. proposed a novel construction paradigm DualRing for RS. In this paper, we first present a DualRing-type LRS scheme $$\textit{Quartet}$$ . Then, we optimize it by using sum arguments of knowledge to a logarithmic size LRS scheme $$\textit{Quartet}^+$$ . Next, we prove the security properties of our schemes, i.e., unforgeability, linkability, anonymity, non-frameability. Finally, we evaluate the performance to illustrate its utility regarding communication and computation costs.
Stable Toffoli Gate on Fixed-Frequency Superconducting Qutrits

Entangling gates in superconducting quantum computers suffer from imperfection of control and relaxation of qubits. The Toffoli gate, essential to various quantum algorithms and to certain fast error correction schemes, represents a particular challenge, as it is decomposed into at least six two-qubit gates, whose errors accrue on top of the increased probability of qubit relaxation due to the long gate time. While it is possible to significantly reduce the depth of the Toffoli gate by exploiting three-level, i.e., qutrit states internally, existing methods are susceptible to errors arising from frequency drifts due to charge fluctuation effects in the second excited state of the transmon. In this poster, we propose a novel qutrit-based implementation of the Toffoli gate that is inherently robust against such errors. The method is extensively validated through a pulse-level simulation and experiments performed on IBM Quantum machines. On one device, where the total Toffoli gate time is 2.510μs, we obtained average gate fidelity values of 0.928 ± 0.007 and 0.896 ± 0.036 from multiple measurements performed during the first one and twenty-four hours after calibrations, respectively, where the errors represent the standard deviations of the measurement results.
Discovering Knowledge Graphs via Attention-Driven Graph Generation

Knowledge graphs are useful in many querying and knowledge management applications, and some difficult data environments have use-cases that would benefit from models that could autonomously create or modify knowledge graphs with context over multiple modalities and/or data-sets. For example, knowledge graph discovery in disconnected data with little or no existing relationship context could provide additional insights and enrich querying into the data. Additionally, a complex query itself may be best represented as a small knowledge graph in some cases. However, there are many challenges in discovering these complex relationships, especially in data-sets that cover multiple modalities. Relationship dependencies may exist at various contextual levels in the data, and some data environments have dynamic data with continuously evolving relationships and/or entities. The focus of the research is to solve challenges in this space by developing novel methods to discover or manipulate knowledge graphs to improve querying and knowledge management in difficult data environments with complex multi-modal relationships.
Beyond Bitcoin: Understanding Legal Boundaries of Blockchain Technology

The current legal uncertainty surrounding blockchain technology can discourage innovators from implementing new blockchain applications. Consequently, it can limitthe development ofthe technology and, ultimately, hinderthe growth ofthis new market. Therefore,this article aims to highlight and address, from a practical standpoint, the prominent legal challenges faced by innovators. The main technical components and applications of blockchain technology – such as crypto-assets, distributed ledgers, and smart contracts – are used as reference points for the analysis. First, the article classifies crypto-assets and specifies (1) when they fall within the scope of banking and finance regulation; and (2) when they fall within the scope ofthe MiCa Regulation proposal (also describing its principles and functioning). Secondly, it investigates the evidential value of distributed ledgers, assesses how they could be exploited in private law matters, and examines the recent proposal of amendment to the eIDAS Regulation. Lastly, it clarifies under which conditions a smart contract can be legally binding and the practical consequences.
Cosmopolitical Interventions: Prototyping Interspecies Encounters for More than Human Futures

Prototyping across the Disciplines - Designing Better Futures; Looks at the use of prototyping in design research across a range of disciplines. Uses case studies to demonstrate theoretical and methodological transferability among disciplines not typically thought to be related to one another. This is an important contribution to the emerging field of integrative design. 89 col. illus.
Experimental Comparison of Collaborative Filtering Algorithm Based on Federal Recommendation

Firstly, we compare the performance of federal recommendation collaborative filtering algorithm model and centralized filtering algorithm model based on different application scenarios. The performance of the federal recommendation collaborative filtering model with different application scenarios in each scenario is compared and studied. Finally, we compare the effects of altered privacy protection strategies on the training process of federal recommendation collaborative filtering model.
Action Spotting in Soccer Videos Using Multiple Scene Encoders

Action spotting, which temporally localizes specific actions in a video, is an important task for understanding high-level semantic information. In this paper, we formulate the action spotting task to one of scene sequence recognition and propose a model with multiple scene encoders to capture scene changes around the timestamp where an action occurs. We divide the input into multiple subsets to reduce the influence of scene context that is temporally distant, and feed every subset into a scene encoder to learn scene context in every subset. Because the optimal temporal length for time windows (chunks) is different for each action, we analyze the influence of chunk sizes for action spotting. The experimental results on the public SoccerNet-v2 dataset demonstrate state-of-the-art accuracy. By using embedding features, our method obtains an Average-mAP of 75.3%. In addition, we confirm that the performance can be improved by using optimal chunk sizes for different actions.
Breaking the Digital Gender Gap with Inclusive Digital Education

Incorporating the gender perspective in STEAM disciplines is essential to reduce the gender gap that has existed to date. The differentiation in STEAM career choice between men and women is based on gender construction. Gender socialisation, and the narrative derived from it, makes women (or girls) feel less able to tackle science careers or professions. One study shows that 5-year-old girls feel less intelligent than their male peers at that age (Bian et al., Science 355(6323):389–391, 2017). In this sense, some data extracted from the Spanish Annual Report on equality figures of the Ministry of Education and Vocational Training 2021, quoted in Macho (Igualdad en cifras MEFP 2021. Mujeres con Ciencia [Women with Science] https://mujeresconciencia.com/2021/04/07/igualdad-en-cifras-mefp-2021/ , 2021) are to be expected, where we find that women choose more studies related to Education (77.9%), Health and Social Services (71.8%). However, there is less presence in Engineering, Industry and Construction (29%) and Computer Science (13.4%). From this perspective, and taking into account that the future will be technological, failing to act within the framework of equitable and egalitarian education and socialisation will result in a greater gender digital divide and, therefore, excluding girls from access to the digital world and the digital future. It is important to highlight the different initiatives carried out with the aim of reducing this gap, but perhaps we should think about intervening through educational inclusion to reduce this gap.
A Study on the Presentation of Immersive Mobile Live Stream Cultural Landscape from the Perspective of Computer-Mediated Communication

The upgrading of media technology brings changes in media forms, and Computer-Mediated Communication (CMC) is constructing the cultural landscape of the new era. Network live stream, which is mainly characterized by technology empowerment, multiple platforms, two-way communication and synchronous interaction, satisfies the sensory experience, interactive experience and emotional experience of the audience. And it further blurs the boundary between virtual and reality. Based on CMC vision, this paper, with the reference of media context theory, aims to explore the psychology and behavior of participants in live-streaming under the state of human-computer interactions. The inner connection among the characteristics of immersive small-screen live-streaming, audience psychology, and behavior would be analyzed. This study attempts to explain the new cultural landscape constructed by online immersive live stream and experience.
Multiview Language Bias Reduction for Visual Question Answering

Current visual question answering models overly rely on language bias and fail to understand visual information sufficiently. Many recent works concentrate on mitigating the intraquestion type bias (bias in the distribution of answers to a question type) without taking the interquestion type bias (bias in distribution between question types) into consideration, causing the model to ignore the tail question types. In addition, they neglect the overall distribution bias of the answer set, leading to the model only focusing on the head answers. In this article, we propose the Multi-View Language Bias Reduction (MVBR) method to solve these problems. For the interquestion type bias, we introduce the Inter-Question Type Bias (IQTB) module. IQTB exploits the question type distribution of the training set to determine the question type bias, which is used to generate weighting factors to reshape the loss of each question type into a balanced form. For the overall distribution bias of the answer set, we utilize the Decoupled Training (DT) module. The DT module penalizes the weights of each class in the classifier and forces their distribution to be balanced. Experimental results demonstrate that MVBR is effective on VQA-CP v2 and effectively improves performance on the mainstream models.
Power dependent dynamics of the 2nd excited state of a Transmon qubit

A qubit, the quantum counterpart of the classical binary digit, can be constructed based on a system with two possible quantum states. For instance, superconducting circuits have been employed to construct an artificial atom in which ground and excited states are used for two quantum states. The anharmonicity of an artificial atom distinguishes the higher-order excited states from two possible quantum states, although they can interact with the cavity as well. The interaction can be important when a large number of photons are allowed in the cavity and results in a nonlinear effect on a Transmon-cavity system. We studied power-dependent nonlinear behaviors of a Transmon-cavity system. The system exhibits dramatic shifts from a dispersive coupling at a low power regime to punch-out behaviors at high powers. This nonlinearity provides us high fidelity readouts for 2nd excited state as well as 1st excited state.[1]
Theory entity extraction for social and behavioral sciences papers using distant supervision

Theories and models, which are common in scientific papers in almost all domains, usually provide the foundations of theoretical analysis and experiments. Understanding the use of theories and models can shed light on the credibility and reproducibility of research works. Compared with metadata, such as title, author, keywords, etc., theory extraction in scientific literature is rarely explored, especially for social and behavioral science (SBS) domains. One challenge of applying supervised learning methods is the lack of a large number of labeled samples for training. In this paper, we propose an automated framework based on distant supervision that leverages entity mentions from Wikipedia to build a ground truth corpus consisting of more than 4500 automatically annotated sentences containing theory/model mentions. We use this corpus to train models for theory extraction in SBS papers. We compared four deep learning architectures and found the RoBERTa-BiLSTM-CRF is the best one with a precision as high as 89.72%. The model is promising to be conveniently extended to domains other than SBS. The code and data are publicly available at https://github.com/lamps-lab/theory.
Web Application Security on Top of Public Cloud

The prevalence of Microservices has made it quintessential to build web applications in a Cloud-Native fashion. While building applications in a cloud-native way, almost the entire infrastructure of an organization relies on an arbitrary Cloud Service Provider’s data center as the individual components of the organization’s on-premise infrastructure are morphed into the modern Infrastructure as a Service(IaaS) model in pay-as-you-go strategy. In this scenario, every Cloud Service Provider(CSP) ensures that they are responsible for securing the data at rest. But the data in transit is left to the user’s responsibility. Some prominent Cloud Service Providers offer services to encrypt the data in-transit as well. But under such circumstances, a copy of our enciphering keys are in any way kept under their premises which in turn is undesirable for many individual users and organizations. So, the solution is to do Client Side Encryption(CSE) to ensure the security ourselves. We are proposing a cryptosystem such that it solidifies the integrity of in-transit data by implementing the Homomorphic encryption technique using a modified form of RSA algorithm. (A study on in-flight data security using cloud services is also done.)
DIT-NET: Joint Deformable Network and Intra-class Transfer GAN for Cross-domain 3D Neonatal Brain MRI Segmentation

In neonatal brain Magnetic Resonance Image (MRI) segmentation, the model we trained on dataset from medical institutions fails to adapt to the clinical data. Because the clinical data (target domain) is largely different from the training dataset (source domain) in terms of scale, shape and intensity distribution. Current GAN-based unsupervised domain adaptation (UDA) models mainly focus on transferring the global intensity distribution, but cannot transform the shape and cover the intra-class similarity. And current deformable network can only transform the shape from the source domain to the target domain, while the intensity cannot be transferred. Thus, the whole cross-domain segmentation can not be trained end-to-end and require complex deformable registration preprocessing. Meanwhile, the deformable preprocessing and UDA method are adversely affected by cross-domain differences in intensity distribution and shape respectively. In this case, we propose a joint framework with Deformable network and Intra-class Transfer GAN (DIT-NET), both of which are trained to perform intra-class intensity and shape transformation for 3D cross-domain segmentation. The Intra-class Transfer GAN is designed not only to intra-classly transfer the intensity for reducing the cross-domain intensity difference, but also to make the deformable network better align the multi-class regions. Our experiments show that the proposed DIT-NET can make the segmentation network better adapt to the target domain end-to-end and it also achieves state-of-the-art results in the comparison with the current UDA models for the 3D neonatal brain MRI segmentation.
Multi-Source Domain Alignment for Domain Invariant Segmentation in Unknown Targets

Semantic segmentation provides scene understanding capability by performing pixel-wise classification of objects within an image. However, the sensitivity of such algorithms towards domain changes requires fine-tuning using an annotated dataset for each novel domain, which is expensive to construct and inefficient. We highlight that irrespective of the training dataset, structural properties of scenes remain the same hence domain sensitivity arises from training methodology. Thus, in this paper, we propose a domain alignment approach wherein multiple synthetic source domains are used to train an underlying segmentation network such that it performs consistently in unknown real target domains. Towards this end, we propose a pixel-wise supervised contrastive learning framework that enforces constraints in latent space resulting in features belonging to the same class being clustered closely and away from different classes. This approach allows for better capturing of global and local semantics while providing domain invariant properties. Our approach can be easily incorporated into prior semantic segmentation approaches without the significant computational overhead. We empirically demonstrate the efficacy of the proposed approach on GTAV → Cityscapes, GTAV+Synthia → Cityscapes, and GTAV+Synthia+Synscapes → Cityscapes scenarios and report state-of-the-art (SoTA) performance without requiring access to images from the target domain.
Multimodal Context Extraction for Virtual Meetings

During the pandemic situation the use of online meetings has increased drastically and in the education and corporate industry it has been a key way to communicate and interact. Huge amount of unprocessed information is present in the videos which can be of great help. Maintaining a report or summary of such virtual meetings manually is tedious and inefficient although many platforms allow recording such meetings but it becomes inefficient to maintain and understand those recordings. A well formatted report summary which can be auto-generated from the recordings and can be easily shared could solve the above issues. To improve the virtual teaching and learning experience system would be generating multiple choice questions based on the extracted keyword from the generated summary.
Reconstructions of Ancient Chinese Revisited: From a Perspective of Similarity Calculation on Doculects

Eight reconstruction systems for Old Chinese and Middle Chinese raised by Bernhard Karlgren, Wang Li, Li Fang-Kuei, Tung T’ungho, Chou Fa-kao, Zhengzhang Shangfang, Pan Wuyun and Baxter-Sagart are revisited in this paper. Taking 60 doculects of balanced Modern Chinese dialects as reference, similarities between Old Chinese, Middle Chinese and modern Chinese dialects are calculated with ASJP tools. Evolution rates for Old Chinese to Middle Chinese, Middle Chinese to Modern Chinese and the rates ratios are analysed. The results show that Zhengzhang Shangfang’s and Baxter-Sagart’s reconstructions are most reasonable and self-consistent ones among the eight systems. The author argues that, from an external perspective of diachronic change of language, analysis on similarity data and change rates may contribute to reconstruction evaluation.
Obfuscation of Documents using Randomly Generated Steps

Data privacy in photos is an important subject that is a major concern in today's world, especially given the compromising data it can contain or the rate at which the information can flow through the Internet. In this paper, we are proposing an approach that can mitigate the danger of sharing things like documents or personal images on the Internet. Our solution to this problem would be to obfuscate these images and documents with a set of randomly generated steps that would alter their content until the owner wants to access them again. Initially, the proposed solution can automatically identify faces, and has support for custom obfuscation on multiple defined areas. After that, it offers the possibility to export the keys separately for each of these areas, allowing at the time of deobfuscation access only to the desired areas.
Stylistic Pattern Guided Tip Extraction from Music Reviews

Reviews are important sources for users to obtain helpful information and make quicker decisions. However, reviews of songs are characterized by a vast number and noise. It is hard for users to grasp meaningful information from a large number of reviews and make decisions. To solve this problem, prior studies propose to provide users with tips. Tips are sentences that are characterized as short, concise, empathetic, and self-contained. Previous studies find that stylistic patterns play an important role in tip identification. However, no prior studies learn the stylistic pattern well. In this paper, we create a heterogeneous graph and propose a dynamic graph convolution net-based model to extract tips by considering stylistic patterns. Experimental results show that our model achieves 74.73% and 73.05% for accuracy and macro F1, outperforming the baseline models by 3.46% and 2.38%.
Clustering Algorithm with Learnable Distance for Categorical Data with Nominal and Ordinal Attributes

For clustering analysis on categorical data, the distance measurement between two objects often plays a very important role. However, most of the existing categorical distance metrics do not distinguish between nominal attributes and ordinal attributes. That is, these metrics do not explore the information contained in ordinal values, and ignore the order relationship among them. Therefore, this paper proposes a novel clustering algorithm, which uses a united framework to measure the distance between nominal attributes and ordinal attributes while distinguishing the different characteristics between them. The basic idea of the proposed method is that, the attribute value pairs with larger co-occurrence probability in the same cluster may have smaller distances. Therefore, the distances between different categories are dynamically evaluated based on the current cluster structure of the data samples. Subsequently, the distances and cluster relationship are alternately learned until convergence. Experimental results show that the proposed algorithm has better robustness and performance than the existing counterparts on different kinds of categorical data sets.