Reviewerly/exHarmony huggingface dataset:
ARCAA

This paper presents ARCAA (Actors, Role, Context, Activity, Artefacts), a framework that supports designers in understanding the artefact ecology in the music performance scenario, in particular, allowing to frame the role of the different actors. The ARCAA framework is the result of the combination of two different areas of HCI: artefact ecology concept, and design framework for digital musical instruments. The model borrows three categories from MINUET an established design framework and rethinks them from an ecological perspective. In ARCAA, these three categories are used as three lenses to connect each human actor to her artefact ecology. Finally, the framework allows comparing how the various artefacts create connections among the different people involved. The second part of the paper describes a case study that shows a practical adoption of the framework.
Scalable Triangle Counting on Distributed-Memory Systems

Triangle counting is a foundational graph-analysis kernel in network science. It has also been one of the challenge problems for the “Static Graph Challenge”. In this work, we propose a novel, hybrid, parallel triangle counting algorithm based on its linear algebra formulation. Our framework uses MPI and Cilk to exploit the benefits of distributed-memory and shared-memory parallelism, respectively. The problem is partitioned among MPI processes using a two-dimensional (2D) Cartesian block partitioning. One-dimensional (1D) rowwise partitioning is used within the Cartesian blocks for shared-memory parallelism using the Cilk programming model. Besides exhibiting very good strong scaling behavior in almost all tested graphs, our algorithm achieves the fastest time on the 1.4B edge real-world twitter graph, which is 3.217 seconds, on 1,092 cores. In comparison to past distributed-memory parallel winners of the graph challenge, we demonstrate a speed up of 2.7× on this twitter graph. This is also the fastest time reported for parallel triangle counting on the twitter graph when the graph is not replicated.
Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer.

Clinical and pathological stage are defining parameters in oncology, which direct a patient's treatment options and prognosis. Pathology reports contain a wealth of staging information that is not stored in structured form in most electronic health records (EHRs). Therefore, we evaluated three supervised machine learning methods (Support Vector Machine, Decision Trees, Gradient Boosting) to classify free-text pathology reports for prostate cancer into T, N and M stage groups.
Inductive and Unsupervised Representation Learning on Graph Structured Objects

Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain. It is also challenging to make graph learning inductive and unsupervised at the same time, as learning processes guided by reconstruction error based loss functions inevitably demand graph similarity evaluation that is usually computationally intractable. In this paper, we propose a general framework SEED (Sampling, Encoding, and Embedding Distributions) for inductive and unsupervised representation learning on graph structured objects. Instead of directly dealing with the computational challenges raised by graph similarity evaluation, given an input graph, the SEED framework samples a number of subgraphs whose reconstruction errors could be efficiently evaluated, encodes the subgraph samples into a collection of subgraph vectors, and employs the embedding of the subgraph vector distribution as the output vector representation for the input graph. By theoretical analysis, we demonstrate the close connection between SEED and graph isomorphism. Using public benchmark datasets, our empirical study suggests the proposed SEED framework is able to achieve up to 10% improvement, compared with competitive baseline methods.
The sentimental fools and the fictitious authors: rethinking the copyright issues of AI-generated contents in China

Artificial Intelligence (AI) technology has reached a point where the contents it generates are ostensibly analogous to expressions, a skill once dominated by humans, is possible. Considering the fact that the concept of ‘author’ is firmly connected with anthropocentrism, the idea of a ‘machine author’ has challenged this understructure of copyright law globally and spawned the need for legislative responses in both common law and civil law jurisdictions. This article investigates the current debates over copyright issues of AI in China, a jurisdiction that embraces the droit d’auteur system, especially those surrounding the pivotal issues of originality and ownership, and clarifies possible misunderstandings. It explains why the subjective path in assessing originality will not work for AI-generated contents, and the necessity of a human host when rejigging the related settings under the Copyright Law of China. In order to solve the structural, theoretical, and practical challenges brought by AI, this article advocates a ‘neighbouring rights’ setting and a digital signature mechanism for regulating AI-generated contents in China.
BERT for Question Generation

In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks.We introduce two neural architectures built on top of BERT for question generation tasks.The first one is a straightforward BERT employment, which reveals the defects of directly using BERT for text generation.And, the second one remedies the first one by restructuring the BERT employment into a sequential manner for taking information from previous decoded results.Our models are trained and evaluated on the question-answering dataset SQuAD.Experiment results show that our best model yields state-of-the-art performance which advances the BLEU 4 score of existing best models from 16.85 to 21.04.
The Hierarchical Effects of Individual and Organizational Variables on Elementary School Teachers Lifelong Learning Competence

The purpose of this study was to analyze the hierarchical effects of individual and organizational variables on elementary school teachers' lifelong learning competence.The participants in this study comprised 1,077 teachers in service in 70 public elementary schools in Seoul, Korea.In this study, 70 schools were sampled using multi-stage stratified sampling, and 10 to 20 teachers were randomly selected for each school.The collected data was analyzed using hierarchical linear modeling.There are three major findings.First, gender, lifelong learning experience, learning agility, learning motivation, and positive psychological capital among the individual variables had meaningful positive effects on lifelong learning competence.Second, knowledge sharing among the organizational variables had meaningful influence on lifelong learning competence.Finally, interactions between gender and knowledge sharing and between learning motivation and learning organization culture had statistically meaningful effects.
Hindi Query Expansion based on Semantic Importance of Hindi WordNet Relations and Fuzzy Graph Connectivity Measures

Query expansion refers to the process of adding terms to a given query for improving the performance of information retrieval (IR). The query might consist of polysemous terms which usually bring down the overall IR performance. To resolve this issue and perform optimized IR, we propose an approach based on fuzzy graphs for Hindi query expansion. To identify additional terms for query, we consider the relative semantic importance of the relations present in Hindi WordNet. The query is represented by the sub-graph extracted from the Hindi WordNet graph. Hindi WordNet is semantically richer due to the presence of a greater number of semantic relations as compared to other WordNets. For all 16 semantic relations present in Hindi WordNet a relative significance score proportional to semantic relatedness is provided. This score acts as the edge weights to the Hindi WordNet graph which is now represented as a fuzzy graph. This assignment helps in moving more semantically related words, closer and recedes away less semantically related words in Hindi WordNet. The selection of significant terms that are to be used for query expansion is done by using local and global fuzzy graph connectivity measures. The proposed method is evaluated on the Forum for Information Retrieval (FIRE) dataset for 3 consecutive years which depicts that the proposed method provides better results than the state-of-art approaches.
Online Hashing with Efficient Updating of Binary Codes

Online hashing methods are efficient in learning the hash functions from the streaming data. However, when the hash functions change, the binary codes for the database have to be recomputed to guarantee the retrieval accuracy. Recomputing the binary codes by accumulating the whole database brings a timeliness challenge to the online retrieval process. In this paper, we propose a novel online hashing framework to update the binary codes efficiently without accumulating the whole database. In our framework, the hash functions are fixed and the projection functions are introduced to learn online from the streaming data. Therefore, inefficient updating of the binary codes by accumulating the whole database can be transformed to efficient updating of the binary codes by projecting the binary codes into another binary space. The queries and the binary code database are projected asymmetrically to further improve the retrieval accuracy. The experiments on two multi-label image databases demonstrate the effectiveness and the efficiency of our method for multi-label image retrieval.
Dual-Layer DNA-Encoding–Decoding Operation Based Image Encryption Using One-Dimensional Chaotic Map

This paper describes a technique that encrypts images in the form of DNA sequences using PWLCM system, i.e., Piecewise Linear Chaotic Map. This method has two times DNA-encoding–decoding operations along with DNA-permutation and DNA-diffusion operations to get the cipher image. On comparison with other processes, the advantage of this algorithm is easy to compute but confuses a cryptanalyst a lot. Apart from that dual-layer DNA-encoding–decoding processes in the algorithm result in good encryption outputs. When outputs are subjected to different security analysis to find out the strength of the algorithm, results with encrypted images with higher values of UACI, NPCR, key space, information entropy, and good correlation coefficient. This results in strong resistivity toward widely used attacks.
Patch Proposal Network for Fast Semantic Segmentation of High-Resolution Images

Despite recent progress on the segmentation of high-resolution images, there exist an unsolved problem, i.e., the trade-off among the segmentation accuracy, memory resources and inference speed. So far, GLNet is introduced for high or ultra-resolution image segmentation, which has reduced the computational memory of the segmentation network. However, it ignores the importances of different cropped patches, and treats tiled patches equally for fusion with the whole image, resulting in high computational cost. To solve this problem, we introduce a patch proposal network (PPN) in this paper, which adaptively distinguishes the critical patches from the trivial ones to fuse with the whole image for refining segmentation. PPN is a classification network which alleviates network training burden and improves segmentation accuracy. We further embed PPN in a global-local segmentation network, instructing global branch and refinement branch to work collaboratively. We implement our method on four image datasets:DeepGlobe, ISIC, CRAG and Cityscapes, the first two are ultra-resolution image datasets and the last two are high-resolution image datasets. The experimental results show that our method achieves almost the best segmentation performance compared with the state-of-the-art segmentation methods and the inference speed is 12.9 fps on DeepGlobe and 10 fps on ISIC. Moreover, we embed PPN with the general semantic segmentation network and the experimental results on Cityscapes which contains more object classes demonstrate the generalization ability on general semantic segmentation.
Virtual Reality for training - The impact of smell on presence, cybersickness, fatigue, stress and knowledge transfer

The area of professional training using virtual reality technologies has received considerable investment due to the advantages that virtual reality provides over traditional training. In this paper, we present an experiment whose goal was to analyse the impact that an additional stimulus has on the effectiveness of a virtual environment designed to train firefighters. The additional stimulus is a smell, more specifically the smell of burnt wood, which is consistent with the audiovisual content presented, and the effectiveness of the VE is measured through participant's feeling of presence, cybersickness, fatigue, stress and transfer of knowledge. The results indicate that, although the VE was successful in transferring knowledge, the addition of smell did not influence any of the measured variables. In the discussion section, we present the various factors that we believe have influenced this result. As future work, more experiments will be performed, with other stimuli, to understand better which stimuli increase participant's feeling of presence in the VE.
A New Constant-Size Group Signature Scheme From Lattices

A lattice-based group signature scheme (LGSS) is an active cryptographic primitive, where each group member can sign messages anonymously in the name of the entire group and each valid signature should be traced to some group member on the lattice. In each LGSS, the size of the group signature usually depends on the number of group members and the security parameter. Thus, designing a constant-size LGSS is an interesting problem. At PKC 2018, Ling, Nguyen, Wang and Xu presented the first constant-size group signature scheme under lattice assumptions. Its design is based on a zero-knowledge argument of the knowledge of a valid message-signature pair for the Ducas-Micciancio signature scheme, which follows the sign-then-encrypt-then-prove protocol. In contrast to this work, we construct a new constant-size LGSS. The scheme adopts the sign-hybrid-encrypt approach and makes use of the Lyubashevsky signature scheme. Our work is efficient in the signing algorithm, more precise on the open algorithm and shorter in public key, secret key and signature size than previous studies. Furthermore, we prove that the scheme has full anonymity and full traceability under the Ring Learning With Errors and Ring Short Integer Solution assumptions in the random oracle model.
A New Algorithm for the Robust Semi-random Independent Set Problem

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)A New Algorithm for the Robust Semi-random Independent Set ProblemTheo McKenzie, Hermish Mehta, and Luca TrevisanTheo McKenzie, Hermish Mehta, and Luca Trevisanpp.738 - 746Chapter DOI:https://doi.org/10.1137/1.9781611975994.45PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We study the independent set problem in a semi-random model proposed by Feige and Kilian. This model selects a graph with a planted independent set of size k and then allows an adversary to modify a large fraction of edges: the subgraph induced by the complement of the independent set can be modified arbitrarily, and the adversary may add (but not delete) edges from the independent set to its complement. In particular, the adversary can create a graph in which the initial planted independent set is not the largest independent set. Feige and Kilian presented a randomized algorithm, which with high probability recovers an independent set of size at least k (which may not be the planted one) when k = an where a is a constant, and the probability of a random edge p > (1 + ϵ) ln n/αn. We give a new deterministic algorithm in the Feige-Kilian model that finds an independent set of size at least .99k provided that the planted set has size k = Ω(n2/3/p1/3), and finds a list of independent sets, one of which is the planted one provided that k = Ω(n2/3/p). This improves on the algorithm of Feige and Kilian by working for smaller k if p = Ω(1/n1/3), and improves on an algorithm of Steinhardt by working for slightly smaller k and by working against a stronger adversarial model. The ability to find a good approximation of the largest independent set is new when p < ln n/k. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Tuz ve Biber Gürültü Kaldırma için Uyarlamalı Cesáro Ortalama Filtresi

In this study, we propound a salt-and-pepper noise (SPN) removal method, i.e.Adaptive Cesáro Mean Filter (ACmF), and provide some of its basic notions.We then apply ACmF to several test images whose noise densities range from 10% to 90%: 15 traditional test images (Baboon, Boat, Bridge, Cameraman, Elaine, Flintstones, Hill, House, Lake, Lena, Living Room, Parrot, Peppers, Pirate, and Plane) and 40 test images, provided in the TESTIMAGES Database.Afterwards, we compare ACmF with the state-of-art methods, such as Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF).The results by The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) show that ACmF performs better than the methods mentioned above.Moreover, we also compare the running time data of these algorithms.These results show that ACmF outperforms the methods except for DAMF.We finally discuss the need for further research.
Blockchain Technology for Emergency Response

As unforeseen situations, emergencies threaten the environment, property, and people's lives.Large emergencies are characterized by the demand for coordination of a variety of actors, such as civil defense or disaster relief.Communication and information exchange are crucial for coordination.Therefore, a solid, stable communication infrastructure is among the crucial factors for emergency response.New technologies that seem to ensure trustworthy communication must be evaluated constantly.Blockchain technology is widely applied in a broad variety of contexts and is commonly known for its decentralized and distributed governance.This is the motivation for the design and evaluation of a framework for the adoption of blockchain technology in the case of emergency response following a design science approach.Evaluation of the artifact using a specific evaluation framework clearly indicates the suitability of the case for application of blockchain technology.
Determining vehicle pre-crash speed in frontal barrier crashes using artificial neural network for intermediate car class

This paper introduces a new, innovative approach to pre-crash velocity determination, namely the artificial neural networks. A perceptron based on a database obtained from NHTSA (National Highway Traffic Safety Administration) with numerous data concerning frontal vehicle crash tests: i.e. vehicle mass, deformation zone and deformation coefficients C1-C6. Part of the database entries were used to train the network to develop consistent accuracy and the remainder was used as validation and training sets.
The OLYMPUS Architecture—Oblivious Identity Management for Private User-Friendly Services

Privacy enhancing technologies (PETs) allow to achieve user’s transactions unlinkability across different online Service Providers. However, current PETs fail to guarantee unlinkability against the Identity Provider (IdP), which becomes a single point of failure in terms of privacy and security, and therefore, might impersonate its users. To address this issue, OLYMPUS EU project establishes an interoperable framework of technologies for a distributed privacy-preserving identity management based on cryptographic techniques that can be applied both to online and offline scenarios. Namely, distributed cryptographic techniques based on threshold cryptography are used to split up the role of the Identity Provider (IdP) into several authorities so that a single entity is not able to impersonate or track its users. The architecture leverages PET technologies, such as distributed threshold-based signatures and privacy attribute-based credentials (p-ABC), so that the signed tokens and the ABC credentials are managed in a distributed way by several IdPs. This paper describes the Olympus architecture, including its associated requirements, the main building blocks and processes, as well as the associated use cases. In addition, the paper shows how the Olympus oblivious architecture can be used to achieve privacy-preserving M2M offline transactions between IoT devices.
What You See is What it Means! Semantic Representation Learning of Code based on Visualization and Transfer Learning

Recent successes in training word embeddings for Natural Language Processing ( NLP ) tasks have encouraged a wave of research on representation learning for source code, which builds on similar NLP methods. The overall objective is then to produce code embeddings that capture the maximum of program semantics. State-of-the-art approaches invariably rely on a syntactic representation (i.e., raw lexical tokens, abstract syntax trees, or intermediate representation tokens) to generate embeddings, which are criticized in the literature as non-robust or non-generalizable. In this work, we investigate a novel embedding approach based on the intuition that source code has visual patterns of semantics. We further use these patterns to address the outstanding challenge of identifying semantic code clones. We propose the WySiWiM ( ‘ ‘What You See Is What It Means ” ) approach where visual representations of source code are fed into powerful pre-trained image classification neural networks from the field of computer vision to benefit from the practical advantages of transfer learning. We evaluate the proposed embedding approach on the task of vulnerable code prediction in source code and on two variations of the task of semantic code clone identification: code clone detection (a binary classification problem), and code classification (a multi-classification problem). We show with experiments on the BigCloneBench (Java), Open Judge (C) that although simple, our WySiWiM approach performs as effectively as state-of-the-art approaches such as ASTNN or TBCNN. We also showed with data from NVD and SARD that WySiWiM representation can be used to learn a vulnerable code detector with reasonable performance (accuracy ∼90%). We further explore the influence of different steps in our approach, such as the choice of visual representations or the classification algorithm, to eventually discuss the promises and limitations of this research direction.
Using of vitagenic technology in the education system

The main idea of vitagenic learning is to form a cooperative relationship between teacher and students. The educational task of a teacher is to diagnose the degree of discrepancy between vitagenic and educational knowledge and, based on a system of scientific evidence, show the educational value of students' life experience. The basis of vitagenic learning is the cultivation of a value attitude not only to Knowledge, but to Ignorance, in which the level of mastery of Knowledge is manifested. This article is written about the need to use vitagenic technology in the education system and the organization of this process.