Reviewerly/exHarmony huggingface dataset:
Fast GPU Implementation of Dumer’s Algorithm Solving the Syndrome Decoding Problem

The syndrome decoding problem (SDP) is a security basis for code-based cryptographic algorithms such as the McEliece cryptosystem, which has been selected as a finalist for the third round of the post-quantum cryptography (PQC) standardization project organized by the National Institute of Standards and Technology (NIST). Information set decoding (ISD) is a general term for algorithms that efficiently solve SDPs based on combinatorial enumeration. In this paper, we focused on Dumer&#x2019;s algorithm, which is relatively lightweight among the ISD algorithms; and proposed a multi-parallel Dumer&#x2019;s algorithm optimized for computing devices that perform massively parallel processing. We implemented our algorithm on a GPU and tested it by solving some SDP instances in the decoding challenge hosted by Inria. We report that our algorithm first solved a 1161-dimensional SDP in the Goppa-McEliece setting in approximately 380 hours, which has a computational complexity of 2<sup>52</sup>. Our results contribute to a more detailed security analysis for code-based cryptosystems.
Blockchain-based Integrity Verification of Data Migration in Multi-cloud Storage

Abstract In the process of multi-cloud storage data migration, data integrity is vulnerable to corruption, but the existing data integrity verification schemes for data migration across clouds are not highly reliable. To address this problem, a blockchain-based data integrity verification scheme for migration across clouds is proposed in this paper. In this scheme, a blockchain network is used instead of a third-party auditor. For each migration, a multi-cloud broker will send an integrity verification request to blockchain at three different times, and a smart contract will verify the data integrity according to the RSA-based homomorphic verification tags. Then, the security of the scheme is analyzed. Finally, simulation experiments and tests are conducted on Ethereum, and the results show the feasibility of the scheme.
Estimation of coronary artery movement using a non-rigid registration with global-local structure preservation

At present, coronary artery disease (CAD) is the leading cause of death worldwide. Many studies have shown that CAD is strongly associated with the motion characteristics of the coronary arteries. Although cardiovascular imaging technology has been widely used for the diagnosis of CAD, the motion parameters of the heart and coronary arteries cannot be directly calculated from the images. In this paper, we propose a point set registration method with global and local topology constraints to quantify coronary artery movement.The global constraint is the motion coherence of the point set which enforces the smoothness of the displacement field. The local linear embedding based topological structure and the local feature descriptor i.e., the 3D shape context, are designed to retain the local structure of the point set. We incorporate these constraints into a maximum likelihood framework and derive an expectation-maximization algorithm to obtain the transformation function between the two point sets. The proposed method was compared with four existing algorithms using simulated data and applied to the real data obtained from 4D CT angiograms.For the simulation data, the proposed method achieves a lower registration error than the comparison algorithms. For the real data, the proposed method shows that, in most cases, the right coronary artery achieves a larger velocity than the left anterior descending and left circumflex branches, and there are three well-defined velocity peaks, during the cardiac cycle for these branches.The proposed approach is feasible and effective in quantifying coronary artery movement and thus adds to the diagnostic power of coronary imaging.
Microsoft Office Macro Warnings:A Design Comedy of Errors with Tragic Security Consequences

The security threat emanating from macro viruses is currently on the rise. Macros are deactivated by default, but when opening a Microsoft Office document with embedded macros, users are presented with a warning message and a one-click option to activate the macro. The aim of the study was to investigate how users interact with this design, to what extent they are aware of the implications of their choices, and how much they know about macros at all. We designed a mixed-methods experiment - consisting of a set of benchmark tasks, knowledge questions, and interviews, which we conducted remotely. To avoid priming participants, the study was advertised as a performance test of a new Outlook Plugin. 36 participants were presented with a naturalistic workflow of emails, some of which contained attachments with macros. We captured how participants interacted with warning messages, and whether they enabled macros. In a subsequent interview, we explored their perception of what had happened, and why they had chosen to enable macros. We found out that 63.9 % of the participants unnecessarily enabled at least one macro when seeing the messages, and that most did not have an accurate mental model of how macros work or the risks associated with opening them. We discuss what elements lead to the enabling of macros and examine them from different perspectives.
Fuzzy Logic-Based Assessment of Students Learning Outcome in Implementing Outcome-Based Education

Academic program accreditation is becoming more demandable all over the world among university students. But the degrees provided by most of the Bangladeshi universities are not accredited because of their deficiency of trained teachers, staff, etc. The Accredited Board of Engineering and Technology (ABET) works on accrediting academic programs and they have some criteria. On the other hand, the evaluation techniques practiced in most Bangladeshi universities are not up to the ABET standards but an absolute grading system. In this research paper, we have proposed a course learning outcome (CLO) and program learning outcome (PLO)-based student performance evaluation technique using the fuzzy logic system. We have considered semester final examinations (SFE) and continuous assessment (CA) consisting of class tests (CT), spot tests, home works, attendance, etc., as evaluation parameters. The course teachers and moderators set question papers assigning marks based on CLOs and the course teachers track the earned marks. Then, the ratios to the earned marks and the assigned marks considering by the CLOs in the SFE and CA are computed and fuzzified. The defuzzification stage returns the attainment of the CLOs of the courses. Finally, the PLOs are also demonstrated using the CLOs. This technique removes the biasedness, unfairness of absolute grading systems. We have case studied for five theoretical courses.
The Many-faced God: Attacking Face Verification System with Embedding and Image Recovery

Face verification system (FVS), which can automatically verify a person’s identity, has been increasingly deployed in the real-world settings. Key to its success is the inclusion of face embedding, a technique that can detect similar photos of the same person by deep neural networks.
Interfaces for Searching and Triaging Large Document Sets: An Ontology-Supported Visual Analytics Approach

We investigate the design of ontology-supported, progressively disclosed visual analytics interfaces for searching and triaging large document sets. The goal is to distill a set of criteria that can help guide the design of such systems. We begin with a background of information search, triage, machine learning, and ontologies. We review research on the multi-stage information-seeking process to distill the criteria. To demonstrate their utility, we apply the criteria to the design of a prototype visual analytics interface: VisualQUEST (Visual interface for QUEry, Search, and Triage). VisualQUEST allows users to plug-and-play document sets and expert-defined ontology files within a domain-independent environment for multi-stage information search and triage tasks. We describe VisualQUEST through a functional workflow and culminate with a discussion of ongoing formative evaluations, limitations, future work, and summary.
Research on rock sample lithology identification algorithm based on ResNet self-supervised learning

The intelligent recognition of rock sample lithology plays an important role in mineral resources exploration. According to the rock sample image, a depth learning model is established. In order to solve the problem of gradient disappearance caused by the excessive depth of neural network, residual structure is introduced, the ResNet structure model is built, and a comparison based self-supervised learning classification algorithm is established, which does not depend on any label value. Using the encoder network to extract features and calculate the reconstruction error in pixel space, we can obtain the ability to identify new samples. The self-supervised lithology recognition algorithm takes resnet18 as the encoder network and the public ImageNet data set as the pre-training data. The parameters are optimized by using the comparative learning gradient descent of positive and negative samples, it adopts a linear classifier, The classification accuracy of rock samples is 85%, which is higher than the classification algorithm based on resnet18 and migration learning, and provides a scientific basis for lithology identification.
Embedded Stochastic Field Exploration with Micro Diving Agents using Bayesian Optimization-Guided Tree-Search and GMRFs

Exploration and monitoring of hazardous fields in marine environments is one of the most promising tasks to be performed by fleets of low-cost micro autonomous underwater vehicles (μAUVs). In contrast to vehicles in other domains, underwater robots are forced to perform all computations onboard as no powerful communication links are available underwater. This puts the focus on computationally efficient field exploration algorithms. We propose CBTS-GMRF – an extremely light-weight tree-search exploration framework suitable for embedded computing. With our framework we build on recent work in POMDP-exploration and field belief representations based on efficient Gaussian Markov random fields (GMRF). We propose a reward function for energy-efficient field exploration together with a sparse trajectory parameterization. By reducing both, energy consumption and computational complexity, we enable underwater field exploration with μAUVs. We benchmark the performance of our exploration framework in simulation against state-of-the-art exploratory planning schemes and provide an experimental study using a low-cost micro diving agent. In order to support community-wide algorithm benchmarking, our code and robot design can be accessed online.
‘Taking a line for a walk’: On improvisatory drawing

In this article we reflect on a line traced by Julia. Julia is an undergraduate student in a class that includes a project entitled ‘Lives of Lines’. As part of the activities of this project, the students were asked to draw continuously for a minute with a white marker on a black page, without lifting the marker, and without trying to represent anything in particular. We analyse Julia’s tracing of the line as a kind of improvisation – the same type of improvising that occurs in conversations, music playing, hiking, dancing and countless other activities. We characterize the improviser as a daydreamer immersed in a reverie: an open field of reciprocating forces, desires, surprises and recollections playing themselves out as some of them encounter their way forward free to proceed, and others do not. The improviser becomes an arena in which body, hand, pen, paper, chair, other bodies, traces, words and sounds mutually displace and attract on their own.
Functional Literacy of School Students as a Factor of Education Quality

The paper aims to ground the pedagogical conditions of school students’ functional literacy formation as an important factor of general education quality. The article supports the idea of the influence of students’ functional literacy on achieving the educational quality significant for life in the modern world. The scientific originality of the research lies in the fact that the experience of educational establishments in students’ functional literacy formation has been systematized based on the pedagogical concepts of students’ functional literacy formation; the criteria, indicators and diagnostic means of revealing professional deficits of teachers in the sphere of students’ functional literacy formation have been developed; the stages of scientific and methodological support of teachers in forming students’ functional literacy based on the tutorial technology have been identified for the first time. As a result, the study has grounded the pedagogical conditions of school students’ functional literacy formation as an important factor of general education quality: 1) realization of educational practices corresponding to the ideas of the participative concept of functional literacy formation; 2) organization of scientific and methodological support of teachers in the sphere of students’ functional literacy formation based on the diagnostics of professional deficits of teachers and the technology of tutorial support.
Linking Entities from Text to Hundreds of RDF Datasets for Enabling Large Scale Entity Enrichment

There is a high increase in approaches that receive as input a text and perform named entity recognition (or extraction) for linking the recognized entities of the given text to RDF Knowledge Bases (or datasets). In this way, it is feasible to retrieve more information for these entities, which can be of primary importance for several tasks, e.g., for facilitating manual annotation, hyperlink creation, content enrichment, for improving data veracity and others. However, current approaches link the extracted entities to one or few knowledge bases, therefore, it is not feasible to retrieve the URIs and facts of each recognized entity from multiple datasets and to discover the most relevant datasets for one or more extracted entities. For enabling this functionality, we introduce a research prototype, called LODsyndesisIE, which exploits three widely used Named Entity Recognition and Disambiguation tools (i.e., DBpedia Spotlight, WAT and Stanford CoreNLP) for recognizing the entities of a given text. Afterwards, it links these entities to the LODsyndesis knowledge base, which offers data enrichment and discovery services for millions of entities over hundreds of RDF datasets. We introduce all the steps of LODsyndesisIE, and we provide information on how to exploit its services through its online application and its REST API. Concerning the evaluation, we use three evaluation collections of texts: (i) for comparing the effectiveness of combining different Named Entity Recognition tools, (ii) for measuring the gain in terms of enrichment by linking the extracted entities to LODsyndesis instead of using a single or a few RDF datasets and (iii) for evaluating the efficiency of LODsyndesisIE.
DFT with Universal Test Set for All Missing Gate Faults in Reversible Circuits

The design of reversible logic circuits has received considerable attention in recent times for their potential use in implementing quantum computers. A fault model, namely, the Missing-Gate Fault (MGF) model, has been found to be more suitable for modeling defects in quantum circuits as compared to the classical fault models used for testing conventional CMOS circuits. In this paper, we propose two design-for-testability techniques with universal test set to detect MGFs in a reversible circuit. In these techniques, an [Formula: see text] reversible circuit implemented with [Formula: see text]-CNOT gates is transformed to a testable design with addition of two extra inputs lines and some additional gates, which uses a universal test set of size [Formula: see text] to detect the MGFs. The proposed DFT techniques are compared with earlier works and it has been shown that these techniques outperform the earlier techniques with respect to quantum cost overhead and percentage detection of the different MGFs.
K-Means Algorithm and Levenshtein Distance Algorithm for Sentiment Analysis of School Zonation System Policy

Equity and quality of education must be guaranteed in the national education system. To that end, the government issued a new student admission policy with a zoning system. To ensure the implementation of new student admissions (PPDB), the zoning system needs to be evaluated for community responses. However, evaluation using conventional techniques still has limitations. Sentiment analysis is a new approach to explore computing-based opinion. In this paper, we conduct a sentiment analysis of the new student admissions system (PPDB) zoning policy. We identify two types of sentiment namely positive and negative. We used the Levenshtein Distance algorithm for word normalization and clustered using the K-Means algorithm. The results of clustering are classified based on the confusion matrix. The data sources that we use are taken from 200 comments on Facebook and Youtube channels. The results obtained from public sentiment towards this policy are more negative sentiments than positive sentiments. The results obtained from the accuracy of the K-Means algorithm are 84%, while the combination of the k-means algorithm with Levenshtein distance reaches 90% accuracy.
PID Control and Fuzzy Logic System to the Obstacle Avoidance in an Autonomous Robot

This paper describes an obstacle avoidance system to a mobile differential robot integrated with 9 infrared sensors, 7 for the detection of obstacles and 2 for the detection of the limit of the environment where the robot will move. A system based on fuzzy logic translates the values read from infrared sensors and adjusts them to position values in degrees. To executing the robot movements with a minimal overshoot was implemented a digital PID control, which allows conduct the robot to the reference degree that the fuzzy system returns. The control system was programmed in the ARM Cortex-M4 STM32F407 microcontroller, using Micropython-OS. The validation tests are described and the results analysis gives a standard deviation of the error of 0.33.
Research on Innovation of Design Education Based on Artificial Intelligence Technology

Abstract The network teaching assistance system uses the form of dialogue to discuss the teaching content with students, the course schedule arrangement, the way and technology of teaching and training, which can effectively improve the teaching quality, shorten the teaching time and increase the teaching efficiency to achieve the established optimization of teaching purposes. In this paper, a forum system for experimental teaching is proposed, which aims to establish a public communication platform to serve the experimental teaching process, and to apply it to the exchange and study between the experimental instructors and students. Through text multimedia technology, the quality of experimental teaching is improved, and students’ practical ability and innovative thinking are cultivated. The experimental teaching forum uses B/S structure to design the interactive platform of information exchange, and presents the information to the user through the browser. The transaction logic is processed by the server side, and the front-end represents the exchange of information.
Integrated ontology-based approach with navigation and content representation for health care website design

• Ontology-based approach is effective for developing a health care website with high usability. • Ontology-based website supports online seeker knowledge-acquisition tasks more effectively than does a text-based website. • Ontology can be used to found website information architecture of health care knowledge. • Navigational mechanisms incorporated with ontology can facilitate task-based wayfinding navigation. • Navigational and representational design mechanisms can be used to display contextual representations.
Several classes of even-variable 1-resilient rotation symmetric Boolean functions with high algebraic degree and nonlinearity

Recent research shows that the class of rotation symmetric Boolean functions is potentially rich in functions of cryptographic significance. In this paper, some classes of 2m-variable (m is an odd integer) 1-resilient rotation symmetric Boolean functions are got, whose nonlinearity and algebraic degree are studied. For the first time, we obtain 2m-variable 1-resilient rotation symmetric Boolean functions having high nonlinearity and optimal algebraic degree. In addition, we obtain a class of non-linear rotation symmetric 1-resilient function for every n≥5, and a class of quadratic rotation symmetric (k−1)-resilient function of n=3k variables, where k is an integer.
A Visualization Model for Silent Cyber Risks Contained in Emerging Risks

As indicated by Industry 4.0, with the advancement of IoT and other technologies, factory equipment with dedicated specifications that were not previously connected to the network are now being replaced by general-purpose equipment and connected to the Internet. However, new risks of being exposed to cyber-attacks have emerged due to attacks from networks and malware planted on general-purpose devices in the supply chain. As such, cyber insurance is now being considered as a new insurance item to deal with silent cyber risk. Silent cyber risks are tricky to recognize, and currently it is difficult to take any security countermeasures in advance other than insurance. In this work, we model and visualize silent cyber risks to enable proactive security countermeasures. We extracted cases that have changed from conventional risk to emerging risk based on the cases that have occurred so far, and developed a state transition model based on previous studies. Specifically, we propose a detailed visualization model of these risks for alteration risks and combination risks. Our evaluation of the proposed model based on actual cases demonstrated its effectiveness.
Robust Image Hashing for Detecting Small Tampering Using a Hyperrectangular Region

In this paper, we propose a robust image hashing method that enables detecting small tampering. Existing hashing methods are too robust, and the trade-off relation between the robustness and the sensitivity to visual content changes needs to be improved to detect small tampering. Though the adaptive thresholding method can improve the trade-off, there&#x0027;s more room to improve and it requires tampered image derived from the original, which limits its applications. To overcome these two drawbacks, we introduce a new concept of a hyperrectangular region in multi-dimensional hash space, which is determined at the timing of hash generation as the region that covers a hash cluster by using the maximum and the minimum of the cluster per each hash axis. We evaluate our method and the existing methods. Our method improves the trade-off, which achieves 0.9428 as AUC (Area Under the Curve) for detecting tampering that occupies about 0.1&#x0025; area of the image in the presence of JPEG compression and reducing the size as content-preserving operations. Furthermore, our method does not require tampered image derived from the original, which differs from the existing method.
Text Based Video Retrieval among Video Clips

In recent years, the popularity of intelligent camera equipment has increased year by year, which has brought the number of videos growing rapidly. It is of practical significance to establish a high-quality video library for the reference of creators. In this paper we describe a video clip retrieval software, which can provide the corresponding video clips according to the needs of video creators such as directors and scriptwriters. The retrieval system is built around the traditional text based image retrieval (i.e. TBIR) method, and on the basis of which many improvements have been made, so that the retrieval speed and accuracy reach a high standard. From the practical experience, it is a successful attempt to apply TBIR to video retrieval.