Reviewerly/exHarmony huggingface dataset:
An Adversarial Training Method for Improving Model Robustness in Unsupervised Domain Adaptation

The easily-perturbed nature of deep neural network makes it vulnerable to adversarial attacks, and such vulnerability has become a major threat to the security of machine learning. The transferability of adversarial samples further increases the threat. Adversarial training has made considerable progress in defending against adversarial samples. In transfer learning, unsupervised domain adaptation is an important research branch, however, due to the label of the target domain samples can’t be obtained, it is difficult to implement adversarial training. In this paper, we found that using source domain data for adversarial training and adding the generated adversarial perturbation to the target domain data could effectively enhance the robustness of the transferred model. Experimental results showed that our proposed method can not only ensure the model’s classification accuracy, but also greatly improve the model’s defense performance against adversarial attacks. In simple, our proposed method not only guarantees the transfer of knowledge, but also realizes the transfer of model robustness. It is the main contribution of this paper.
Global Entity Alignment with Gated Latent Space Neighborhood Aggregation

Existing entity alignment models mainly use the topology structure of the original knowledge graph and have achieved promising performance. However, they are still challenged by the heterogeneous topological neighborhood structures, which could cause the models to produce different representations of counterpart entities. In the paper, we propose a global entity alignment model with gated latent space neighborhood aggregation (LatsEA) to address this challenge. Latent space neighborhood is formed by calculating the similarity between the entity embeddings, it can introduce long-range neighbors to expand the topological neighborhood and reconcile the heterogeneous neighborhood structures. Meanwhile, it uses vanilla GCN to aggregate the topological neighborhood and latent space neighborhood respectively. Then, it uses an average gating mechanism to aggregate topological neighborhood information and latent space neighborhood information of the central entity. In order to further consider the interdependence between entity alignment decisions, we propose a global entity alignment strategy, i.e., formulate entity alignment as the maximum bipartite matching problem, which is effectively solved by Hungarian algorithm. Our experiments with ablation studies on three real-world entity alignment datasets prove the effectiveness of the proposed model. Latent space neighborhood information and global entity alignment decisions both contributes to the entity alignment performance improvement.
ShExMap and IPSM-AF—Comparison of RDF Transformation Technologies

RDF is growing in popularity for enterprise data harmonization software (e.g. Virtuoso, or Corporate Memory).Szmeja, Paweł Interoperability techniques, e.g. query rewriting, and language transformation rely on the expressivity and flexibility of RDF.Prud’hommeaux, Eric As a meta-model capable of expressing information with the use of a variety of vocabularies or ontologies, RDF enables a range of solutions for the problem of data interoperability. Exploiting RDF for interoperability surfaces the need for transformations between homologous RDF structures. This paper examines two independently developed technologies that enable mapping of RDF graphs: Shape Expressions Mapping Language (ShExMap) and IPSM (Inter Platform Semantic Mediator). We investigate the formal languages used by both, examining their theoretical foundation, peculiarities, and design. We consider two practical examples of transformations and compare effectively equivalent constructs that can be expressed in either approach. We also describe both technologies in broader context of practical applicability.
BESA: BERT-based Simulated Annealing for Adversarial Text Attacks

Modern Natural Language Processing (NLP) models are known immensely brittle towards text adversarial examples. Recent attack algorithms usually adopt word-level substitution strategies following a pre-computed word replacement mechanism. However, their resultant adversarial examples are still imperfect in achieving grammar correctness and semantic similarities, which is largely because of their unsuitable candidate word selections and static optimization methods. In this research, we propose BESA, a BERT-based Simulated Annealing algorithm, to address these two problems. Firstly, we leverage the BERT Masked Language Model (MLM) to generate contextual-aware candidate words to produce fluent adversarial text and avoid grammar errors. Secondly, we employ Simulated Annealing (SA) to adaptively determine the word substitution order. The SA provides sufficient word replacement options via internal simulations, with an objective to obtain both a high attack success rate and a low word substitution rate. Besides, our algorithm is able to jump out of local optima with a controlled probability, making it closer to achieve the best possible attack (i.e., the global optima). Experiments on five popular datasets manifest the superiority of BESA compared with existing methods, including TextFooler, BAE, BERT-Attack, PWWS, and PSO.
Diagnostic Instrument of the Level of Competencies in Cloud Computing for Teachers in Education 4.0

Universities face a paradigmatic educational challenge, driven by Industry 4.0 technologies such as Cloud Computing, producing the need to develop new teaching competencies. The question arises: How can we diagnose the level of Cloud Computing competencies that teachers have in the context of Education 4.0? To address this question, the objective was to design a valid and reliable instrument to measure these competencies. A literature review and expert collaboration were carried out. For validation, it was submitted to expert judgment and Kendall's W concordance coefficient was calculated; for reliability, a pilot test was carried out with teachers of the Computer Systems Engineering career and the KR20 (Kuder-Richardson) was used. The result was an instrument with 23 items capable of measuring seven competencies identified by the collaborative network Industry 4.0 Node. This instrument contributes as a guideline for educational institutions, bridging the gap between technological progress and formal education.
Identity-Based Chameleon Hash without Random Oracles and Application in the Mobile Internet

The rapid development of the mobile Internet makes it necessary to adopt efficient cryptographic primitives for the portable devices with limited computing resources. Online/offline identity-based signatures are suitable because of short response time of signature generation and being free from the cumbersome operations caused by public key infrastructures. In this paper, we propose the first identity-based chameleon hash which can be proved secure without the random oracle and show how to use it to translate any identity-based signature to an online/offline one.
Feature Matching Synchronized Reasoning from Energy-Based Memory Network for Intelligent Data Management in Cloud Computing Data Center

A cloud data center for software-as-a-service (SaaS) was built for the purpose of stably managing these server computers in one place in order to provide an uninterrupted service, not only for a stable power supply and security but also for the efficient data management. To manage such a data center efficiently, it is important to build a cloud database with structured storage above all else. In recent decades, many studies have focused on designing cloud data centers and most of the research has focused on communication traffic, routing, topological issues and communication technology. However, in order to build an efficient cloud database that can support user demand, the most sophisticated intelligent system, based on AI technology and considering user convenience, should be designed. From this viewpoint, adopting human brain functions, Energy-Based Memory Network was designed for a knowledge-based frame of an intelligent system. And its event-related synchronized data extraction mechanism was proposed. In particular, a Thinking Thread extraction phase was implemented for the reasoning process using qualia matching and a deep extraction method in a cloud database. The purpose of this approach is to design and implement an intelligent cloud database that has an efficient structure and mechanism for supporting user demand and providing accurate, prompt services. In experiments, the working phase of the functions was simulated with data and analyzed. As a result, it was confirmed that the proposed system works well and intelligently for the design purpose.
Populating Web-Scale Knowledge Graphs Using Distantly Supervised Relation Extraction and Validation

In this paper, we propose a fully automated system to extend knowledge graphs using external information from web-scale corpora. The designed system leverages a deep-learning-based technology for relation extraction that can be trained by a distantly supervised approach. In addition, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations. The designed system does not require any effort for adaptation to new languages and domains as it does not use any hand-labeled data, NLP analytics, and inference rules. Our experiments, performed on a popular academic benchmark, demonstrate that the suggested system boosts the performance of relation extraction by a wide margin, reporting error reductions of 50%, resulting in relative improvement of up to 100%. Furthermore, a web-scale experiment conducted to extend DBPedia with knowledge from Common Crawl shows that our system is not only scalable but also does not require any adaptation cost, while yielding a substantial accuracy gain.
Value of ddPCR in the Preoperative Diagnosis of Solitary Pulmonary Nodules Based on the Observation of Virtual Reality Images of Smart Medical Treatment

At present, lung cancer ranks the first cause of tumor death in the world, and malignant tumors in the SPN detected by imaging account for 5-40%, most of which are peripheral lung cancer. The recovery of the solitary nodules in the lung after treatment has also been paid attention to. In order to explore the correlation of solitary pulmonary nodule (SPN) with microvessel density (MVD), vascular parameters, and vascular ratio under virtual reality images of smart medical treatment and evaluate the differentiation of SPN by ddPCR under virtual reality image observation diagnosis value, this article collects relevant information by investigating patients, investigating relevant literature, interviewing professionals, and constructing a case template, using a comprehensive quantitative and qualitative analysis method to create a damage assessment matrix. Experimental results prove that there are significant differences in the microvascular architecture within the SPN in the benign, inflammatory, and malignant groups. The correlation between ddPCR and vascular parameters (especially the ratio of luminal vessels) under the virtual reality image observation of smart medical treatment is better than other detection methods, and its accuracy is about 10% higher. Based on the observation of smart medical virtual reality images, ddPCR can be used as an index for noninvasive evaluation of tumor angiogenesis, which is helpful for the differential diagnosis of SPN.
Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks

An attack on deep learning systems where intelligent machines collaborate to solve problems could cause a node in the network to make a mistake on a critical judgment. At the same time, the security and privacy concerns of AI have galvanized the attention of experts from multiple disciplines. In this research, we successfully mounted adversarial attacks on a federated learning (FL) environment using three different datasets. The attacks leveraged generative adversarial networks (GANs) to affect the learning process and strive to reconstruct the private data of users by learning hidden features from shared local model parameters. The attack was target-oriented drawing data with distinct class distribution from the CIFAR-10, MNIST, and Fashion-MNIST respectively. Moreover, by measuring the Euclidean distance between the real data and the reconstructed adversarial samples, we evaluated the performance of the adversary in the learning processes in various scenarios. At last, we successfully reconstructed the real data of the victim from the shared global model parameters with all the applied datasets.
Accelerating Neural Architecture Search via Proxy Data

Despite the increasing interest in neural architecture search (NAS), the significant computational cost of NAS is a hindrance to researchers. Hence, we propose to reduce the cost of NAS using proxy data, i.e., a representative subset of the target data, without sacrificing search performance. Even though data selection has been used across various fields, our evaluation of existing selection methods for NAS algorithms offered by NAS-Bench-1shot1 reveals that they are not always appropriate for NAS and a new selection method is necessary. By analyzing proxy data constructed using various selection methods through data entropy, we propose a novel proxy data selection method tailored for NAS. To empirically demonstrate the effectiveness, we conduct thorough experiments across diverse datasets, search spaces, and NAS algorithms. Consequently, NAS algorithms with the proposed selection discover architectures that are competitive with those obtained using the entire dataset. It significantly reduces the search cost: executing DARTS with the proposed selection requires only 40 minutes on CIFAR-10 and 7.5 hours on ImageNet with a single GPU. Additionally, when the architecture searched on ImageNet using the proposed selection is inversely transferred to CIFAR-10, a state-of-the-art test error of 2.4% is yielded. Our code is available at https://github.com/nabk89/NAS-with-Proxy-data.
Signal Processing on the Permutahedron: Tight Spectral Frames for Ranked Data Analysis

Ranked data sets, where m judges/voters specify a preference ranking of n objects/candidates, are increasingly prevalent in contexts such as political elections, computer vision, recommender systems, and bioinformatics. The vote counts for each ranking can be viewed as an n! data vector lying on the permutahedron, which is a Cayley graph of the symmetric group with vertices labeled by permutations and an edge when two permutations differ by an adjacent transposition. Leveraging combinatorial representation theory and recent progress in signal processing on graphs, we investigate a novel, scalable transform method to interpret and exploit structure in ranked data. We represent data on the permutahedron using an overcomplete dictionary of atoms, each of which captures both smoothness information about the data (typically the focus of spectral graph decomposition methods in graph signal processing) and structural information about the data (typically the focus of symmetry decomposition methods from representation theory). These atoms have a more naturally interpretable structure than any known basis for signals on the permutahedron, and they form a Parseval frame, ensuring beneficial numerical properties such as energy preservation. We develop specialized algorithms and open software that take advantage of the symmetry and structure of the permutahedron to improve the scalability of the proposed method, making it more applicable to the high-dimensional ranked data found in applications.
Plenary Debates of the Parliament of Finland as Linked Open Data and in Parla-CLARIN Markup.

This paper presents a knowledge graph created by transforming the plenary debates of the Parliament of Finland (1907-) into Linked Open Data (LOD). The data, totaling over νm{900 000} speeches, with automatically created semantic annotations and rich ontology-based metadata, are published in a Linked Open Data Service and are used via a SPARQL API and as data dumps. The speech data is part of larger LOD publication FinnParla that also includes prosopographical data about the politicians. The data is being used for studying parliamentary language and culture in Digital Humanities in several universities. To serve a wider variety of users, the entirety of this data was also produced using Parla-CLARIN markup. We present the first publication of all Finnish parliamentary debates as data. Technical novelties in our approach include the use of both Parla-CLARIN and an RDF schema developed for representing the speeches, integration of the data to a new Parliament of Finland Ontology for deeper data analyses, and enriching the data with a variety of external national and international data sources.
ReflectPal: Exploring Self-Reflection on Collaborative Activities Using Voice Assistants

Voice Assistants (VAs) present promising opportunities for the development of applications for the work domain. While previous research is primarily focused on aiding groups and individuals to be more productive, studies exploring the use of VAs to train and develop collaboration skills are rather limited. In this paper, we examine whether VAs can be used to help individuals improve their collaboration skills through self-reflection. We developed ReflectPal, a Google Assistant application designed to facilitate reflection sessions regarding collaboration challenges. First, we identified a list of frequently occurring challenges in a specific collaboration work environment. Then we designed ReflectPal to address a subset of those challenges and tested it in a two-week in-situ deployment with 19 participants. We found that participants benefited from the structure that the practice provided, leading to deeper and more meaningful reflections than before. However, the study also highlighted the need to design applications that take motivational aspects into account to encourage frequent engagement in self-reflection for skill development. Reflecting on insights from our study, we discuss future design directions of VAs for facilitating self-reflection in a collaborative work context.
An adaptive cryptosystem on a Finite Field

Owing to mathematical theory and computational power evolution, modern cryptosystems demand ingenious trapdoor functions as their foundation to extend the gap between an enthusiastic interceptor and sensitive information. This paper introduces an adaptive block encryption scheme. This system is based on product, exponent, and modulo operation on a finite field. At the heart of this algorithm lies an innovative and robust trapdoor function that operates in the Galois Field and is responsible for the superior speed and security offered by it. Prime number theorem plays a fundamental role in this system, to keep unwelcome adversaries at bay. This is a self-adjusting cryptosystem that autonomously optimizes the system parameters thereby reducing effort on the user's side while enhancing the level of security. This paper provides an extensive analysis of a few notable attributes of this cryptosystem such as its exponential rise in security with an increase in the length of plaintext while simultaneously ensuring that the operations are carried out in feasible runtime. Additionally, an experimental analysis is also performed to study the trends and relations between the cryptosystem parameters, including a few edge cases.
CemoMemo

Digitizing cemeteries and gravestones aids cultural preservation, genealogical search, dark tourism, and historical analysis. CemoMemo, an app and associated website, enables bottom-up crowd-sourced digitization of cemeteries, categorizing and indexing of gravestone data and metadata, and offering powerful full-text and numerical search. To date, CemoMemo has nearly 5,000 graves from over 130 cemeteries in 10 countries with the majority being Jewish graves in Israel and the USA. We detail CemoMemo's deployment and component models, technical attributes, and user models. CemoMemo went through two design iterations and architectures. We detail its initial architecture and the reasons that led to the change in architecture. To show its utility, we use CemoMemo's data for a historical analysis of two Jewish cemeteries from a similar period, eliciting cultural and ethnological difference between them. We present lessons learned from developing and operating CemoMemo for over 1 year and point to future directions of development.
TactCube: Designing Mobile Interactions with Ambient Intelligence

This paper presents TactCube, a novel tangible user interface based on a cube-shaped device. It is designed to interact with Ambient Intelligence and allows users to give commands and receive rich feedback using only one hand. A guessability user study was conducted involving 60 participants and results were used to map the device interactions onto commands invoked on an Ambient Intelligence system. The study considered three smart devices, properly a smart speaker, a smart bulb, and a smart curtain. For each of the devices, users were asked to propose his/her favorite interaction modality through TactCube, for 12 different commands and a kind of admissible feedback for 8 different states of the ambient intelligence system. The analysis of the responses led us to derive a set of interaction design implications which could be taken into account for any tangible interface inside an intelligent ambient system.
Detector‐device‐independent quantum secret sharing based on hyper‐encoding and single‐photon Bell‐state measurement

Multiparty quantum communication attracts continuous attention due to the better adaptability for the future quantum internet than point-to-point communication. Unfortunately, the imperfections of practical devices are exploited to take side-channel attacks which threatens the security of practical multiparty quantum communication. We propose a three-party detector-device-independent quantum secret sharing (DDI-QSS) protocol based on hyper-encoding and single-photon Bell-state measurement. It works in a deterministic way. The hyper-encoding circuit and single-photon Bell-state measurement device can be built with only linear optical elements, which makes the protocol more practical. Different from the measurement-device-independent setting, the DDI-QSS protocol does not require the multi-photon interference of photons from independent sources. We prove that it is secure against general entanglement-measure attacks launched by a dishonest participant.