Reviewerly/exHarmony huggingface dataset:
Evaluation Exercise in the Seminar for Standard Test Method of Ground Robot at Fukushima Robot Test Field

A seminar to study the standard test method of the robot which can gather information at disaster sites and survey infrastructure have been held since 2018 in Japan by NEDO and MSTC. The author of this paper is one of teachers of the seminar about test methods for ground robots. In this paper, the evaluation exercises in the simulated plant and tunnel at the Fukushima Robot Test Field are reported. The attendees of the exercises can deeply and completely understand the test methods by the experience in the exercises.
A Study of Machine Reading Comprehension Based on Attention Mechanism

Reading comprehension is one of the most critical abilities in human intelligence, and machine reading comprehension is one of the most popular research directions in the field of natural language processing. Machine reading is one of the key aspects to achieve artificial intelligence. This paper first analyzes the current state of development of machine reading comprehension, and based on the current problems in machine reading comprehension, this paper applies various attention mechanisms in the reading comprehension model, so that the information of articles and questions can be fused with each other to extract the key information and predict the answers more accurately. This paper also designs experiments and verifies that this method has a higher accuracy rate compared with other methods.
Augmented Reality as a Mediator for Emotionally Engaging Stories

With the veterans who fought in World War II gradually fading away, their stories and the memory associated with war events risk to be lost forever. To help prevent this from happening, Mobile Augmented Reality (MAR) applications were developed for 11 of the 75 personal life-changing war stories that had been selected as part of the Brabant Remembers project. This paper presents the results of a study to understand whether AR plays a role in providing emotional engagement and memorable experiences to visitors using these apps. It concerns a case study relative to the monument in Breda commemorating General Maczek, a Polish general whose army liberated the city and the southern part of the Netherlands in 1944. The results of this study help understand whether AR enables people to emotionally engage with these stories and have memorable experiences, what will allow to remember them longer.
Group signature with time-bound keys and unforgeability of expiry time for smart cities

Abstract Internet of Things (IoT) lays the foundation for the various applications in smart cities, yet resource-constrained IoT devices are prone to suffer from devastating cyberattacks and privacy leak threats, thus are inevitability supposed as the weakest link of the systems in smart cities. Mitigating the security risks of data and the computing limitation of edge devices, especially identity authentication and key validity management of group devices are essential for IoT system security. In order to tackle the issues of anonymity, traceability, unforgeability of expiry time as well as efficient membership revocation for life-cycle management of devices in IoT setting, we presented a dynamic time-bound group signature with unforgeability of expiry time. Unforgeability of expiry time disables a revoked signer to create a valid signature by means of associating the signing key with an expiry time. The anonymity and traceability of the proposed scheme contribute to the identity privacy of the entities and supervision for authority agency. Moreover, our proposal is feasible in the resource-constrained setting for efficient computational cost of signing and verification algorithms.
BETA: Biometric-Enabled Threshold Authentication

In the past decades, user authentication has been dominated by server-side password-based solutions that rely on “what users know”. This approach is susceptible to breaches and phishing attacks, and poses usability challenges. As a result, the industry is gradually moving to biometric-based client-side solutions that do not store any secret information on servers. This shift necessitates the safe storage of biometric templates and private keys, which are used to generate tokens, on user devices.We propose a new generic framework called Biometric Enabled Threshold Authentication (BETA) to protect sensitive client-side information like biometric templates and cryptographic keys. Towards this, we formally introduce the notion of Fuzzy Threshold Tokenizer (\(\text {FTT}\)) where an initiator can use a “close” biometric measurement to generate an authentication token if at least t (the threshold) devices participate. We require that the devices only talk to the initiator, and not to each other, to capture the way user devices are connected in the real world. We use the universal composability (UC) framework to model the security properties of \(\text {FTT}\), including the unforgeability of tokens and the privacy of the biometric values (template and measurement), under a malicious adversary. We construct three protocols that meet our definition.Our first two protocols are general feasibility results that work for any distance function, any threshold t and tolerate the maximal (i.e. \(t-1\)) amount of corruption. They are based on any two round UC-secure multi-party computation protocol in the standard model (with a CRS) and threshold fully homomorphic encryption, respectively. We show how to effectively use these primitives to build protocols in a constrained communication model with just four rounds of communication.For the third protocol, we consider inner-product based distance metrics (cosine similarity, Euclidean distance, etc.) specifically, motivated by the recent interest in its use for face recognition. We use Paillier encryption, efficient NIZKs for specific languages, and a simple garbled circuit to build an efficient protocol for the common case of \(n=3\) devices with one compromised.
Integrating AR/MR/DR technology in remote seal to maintain confidentiality of information

(2021). Integrating AR/MR/DR technology in remote seal to maintain confidentiality of information. Advanced Robotics: Vol. 35, Extra special issue on Soft/Social/Systemic (3S) Robot Technologies for enhancing Quality of New Normal (QoNN), pp. 704-714.
Development of Professional Skills in the Context of Higher School Dual Education

The article considers aspects of the dual education system which are implemented in advanced higher education institutions of the Republic of Kazakhstan. The ranking of the best universities for the implementation of the dual education sys-tem is analyzed within the framework of previous studies on the matter. It is not-ed that in the system of dual education a significant role is given to the formation of the personality of the specialist. It is emphasized that dual education is aimed at raising awareness of students' educational and professional activities. The main competences of the student at different levels (practical training, content and structure of educational programs, level of mutual trust, level of adaptation to the work process, level of learning motivation, level of precision in understanding the profession, level of standardized training) are described. The importance of prac-tical orientation of the education system based on acquired competences (solving real production problems, reforming research work, participation in scientific and practical conferences) is emphasized. Recommendations on the process of intro-ducing dual education are provided and models are offered for classification of professional development resources, components of conceptual model, principles of formation of the structure and content of the educational programs, indicators of professional skills, and indicators of professional skills development.
Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES

In this paper, we investigate a non-elitist Evolution Strategy designed to handle black-box constraints by an adaptive Augmented Lagrangian penalty approach, AL-(μ/μw, λ)-CMA-ES, on problems with up to 28 constraints. Based on stability and performance observations, we propose an improved default parameter setting. We exhibit failure cases of the Augmented Lagrangian technique and show how surrogate modeling of the constraints can overcome some difficulties. Several variants of AL-CMA-ES are compared on a set of nonlinear constrained problems from the literature. Simple adaptive penalty techniques serve as a baseline for comparison.
Stitched open-loop measurements with a focal-distance-modulated confocal sensor

Abstract The article presents measurements with a high-speed focal-distance modulated fibre-coupled confocal sensor integrated into a nano measuring machine (NMM-1). Combined with an appropriate signal processing, this axial beam modulating sensor yields a linear characteristic curve within a range of about 600 nm. This characteristic curve enables scans in closed-loop constant distance mode, where the controller of the NMM-1 keeps the distance between the measured surface and the sensor constant. Therefore, only one lateral scan is necessary to measure a given topography. Nevertheless, it is also possible to conduct open-loop constant height measurements on discrete heights. The axial offset between the heights needs to be only slightly less than the measuring range of the sensor, leading to a great reduction of the number of needed lateral scans to measure a given topography compared to a conventional confocal sensor, where the axial offset for a similar optical system is typically about 50 nm. To combine the scans taken at different heights, it is possible to stitch them using overlapping measuring points taken at two adjacent heights. Within this article, measurements are conducted on a roughness standard in closed-loop constant distance and in open-loop constant height mode, where the latter are evaluated with and without the application of the stitching algorithm.
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings

We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical embeddings from prior training iterations, leading to constant GPU memory consumption in respect to input node size without dropping any data. While existing solutions weaken the expressive power of message passing due to sub-sampling of edges or non-trainable propagations, our approach is provably able to maintain the expressive power of the original GNN. We achieve this by providing approximation error bounds of historical embeddings and show how to tighten them in practice. Empirically, we show that the practical realization of our framework, PyGAS, an easy-to-use extension for PyTorch Geometric, is both fast and memory-efficient, learns expressive node representations, closely resembles the performance of their non-scaling counterparts, and reaches state-of-the-art performance on large-scale graphs.
Development of the Russian Universities’ Human Capacity in the Field of Mathematics, Computer Science, and Digital Technologies

The paper is devoted to the pilot implementation of CPD programs for universities academic staff advanced training in the International Computer Science Continuous Professional Development Centre of Tomsk State University. The Centre was created within the framework of the project “Human Resources for the Digital Economy” of the national program “Digital Economy of the Russian Federation”. The article presents the structure of CPD programs by tracks: research and development, advanced educational technologies and academic programs of a new generation in the IT field. The authors discuss the results of online training of more than 1000 academic stuff members from more than 100 universities for 4 months in the second half of 2020.
An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote

In supervised learning, classifiers range from simpler, more interpretable and generally less accurate ones (e.g., CART, C4.5, J48) to more complex, less interpretable and more accurate ones (e.g., neural networks, SVM). In this tradeoff between interpretability and accuracy, we propose a new classifier based on association rules, that is to say, both easy to interpret and leading to relevant accuracy. To illustrate this proposal, its performance is compared to other widely used methods on six open access datasets.
Nesnelerin İnterneti Aygıtlarının Görüşmelerinin Blok Zinciri

The world of IoT devices grows day by day. The interaction of IoT devices increases day by day. Interactions of IoT devices become valuable for many applications. Each special and spatial interaction of IoT devices is a meeting for them. An IoT meeting requires that they should see each other directly via a short-distance communication channel. In this work, meetings of IoT devices are kept and exchanged with others. IoT meeting transactions should be reliable. For this purpose, a special blockchain is proposed, and formation mechanism of IoT meetings is designed accordingly. The proposed blockchain uses Proof-of-Work consensus mechanism to mine meeting transactions. Transactions are signed by IoT devices against data tampering. In the blockchain, 5 types of actors are formed according to their own resources. These are meeting declarer, meeting confirmer, data requester, data provider, and miner. The blockchain transactions are designed to protect privacy and rights of the actors. Moreover, the blockchain has its own cryptocurrency. Actors other than data requester are rewarded with cryptocurrency to increase the willingness of data sharing and to increase participation in forming meeting transactions.
Design of intelligent acquisition system for moving object trajectory data under cloud computing

Abstract In order to study the intelligent collection system of moving object trajectory data under cloud computing, information useful to passengers and taxi drivers is collected from massive trajectory data. This paper uses cloud computing technology, through clustering algorithm and density-based DBSCAN algorithm combined with Map Reduce programming model and design trajectory clustering algorithm. The results show that based on the 8-day data of 15,000 taxis in Shenzhen, the characteristic time period is determined. The passenger hot spot area is obtained by clustering the passenger load points in each time period, which verifies the feasibility of the passenger load point recommendation application based on trajectory clustering. Therefore, in the absence of holidays, the number of passenger hotspots tends to be stable. It is reliable to perform cluster analysis. The recommended application has been demonstrated through experiments, and the implementation results show the rationality of the recommended application design and the feasibility of practice.
Lifetime Maximization of an Internet of Things (IoT) Network Based on Graph Signal Processing

The lifetime of an Internet of Things (IoT) system consisting of battery-powered devices can be increased by minimizing the number of transmissions per device while not excessively deteriorating the correctness of the overall IoT monitoring. We propose a graph signal processing based algorithm for partitioning the sensor nodes into disjoint sampling sets. The sets can be sampled on a round-robin basis and each one contains enough information to reconstruct the entire signal within an acceptable error bound. Simulations on different models of graphs, based on graph theory and on real-world applications, show that our proposal consistently outperforms state-of-the-art sampling schemes, with no additional computational burden.
A Comparative Analysis Using Different Machine Learning: An Efficient Approach for Measuring Accuracy of Face Recognition

Feature extracting and training module can be done by using face recognition neural learning techniques. Moreover, these techniques are widely employed to extract features from human images. Some detection systems are capable to scan the full body, iris detection, and finger print detection systems. These systems have deployed for safety and security intension. In this research work, we compare different machine learning algorithms for face recognition. Four supervised face recognition machine-learning classifiers such as Principal Component Analysis (PCA), 1-nearest neighbor (1-NN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are considered. The efficiency of multiple classification systems is also demonstrated and tested in terms of their ability to identify a face correctly. Face Recognition is a technique to identify faces of people whose images are stored in some databases and available in the form of datasets. Extensive experiments conducted on these datasets. The comparative analysis clearly shows that which machine-learning algorithm is the best in terms of accuracy of image detection. Despite the fact, other identification methods are also very effective; face recognition has remained a major focus of research due to its non-meddling nature and being the easy method of personal identification for people. The findings of this work would be useful identification of a suitable machine-learning algorithm in order to achieve better face recognition accuracy.
mRelief: A Reward Penalty Based Feature Subset Selection Considering Data Overlapping Problem

Feature selection plays a vital role in machine learning and data mining by eliminating noisy and irrelevant attributes without compromising the classification performance. To select the best subset of features, we need to consider several issues such as the relationship among the features (interaction) and their relationship with the classes. Even though the state-of-the-art, Relief based feature selection methods can handle feature interactions, they often fail to capture the relationship of features with different classes. That is, a feature that can provide a clear boundary between two classes with a small average distance may be mistakenly ranked low compared to a feature that has a higher average distance with no clear boundary (data overlapping). Moreover, most of the existing methods provide a ranking of the given features rather than selecting a proper subset of the features. To address these issues, we propose a feature subset selection method namely modified Relief (mRelief) that can handle both feature interactions and data overlapping problems. Experimental results over twenty-seven benchmark datasets taken from different application areas demonstrate the superiority of mRelief over the state-of-the-art methods in terms of accuracies, number of the selected features, and the ability to identify the features (gene) to characterize a class (disease).
Virtual Archaeology of Death and Burial: A Procedure for Integrating 3D Visualization and Analysis in Archaeothanatology

Abstract The reconstruction of past mortuary rituals and practices increasingly incorporates analysis of the taphonomic history of the grave and buried body, using the framework provided by archaeothanatology. Archaeothanatological analysis relies on interpretation of the three-dimensional (3D) relationship of bones within the grave and traditionally depends on elaborate written descriptions and two-dimensional (2D) images of the remains during excavation to capture this spatial information. With the rapid development of inexpensive 3D tools, digital replicas (3D models) are now commonly available to preserve 3D information on human burials during excavation. A procedure developed using a test case to enhance archaeothanatological analysis and improve post-excavation analysis of human burials is described. Beyond preservation of static spatial information, 3D visualization techniques can be used in archaeothanatology to reconstruct the spatial displacement of bones over time, from deposition of the body to excavation of the skeletonized remains. The purpose of the procedure is to produce 3D simulations to visualize and test archaeothanatological hypotheses, thereby augmenting traditional archaeothanatological analysis. We illustrate our approach with the reconstruction of mortuary practices and burial taphonomy of a Bell Beaker burial from the site of Oostwoud-Tuithoorn, West-Frisia, the Netherlands. This case study was selected as the test case because of its relatively complete context information. The test case shows the potential for application of the procedure to older 2D field documentation, even when the amount and detail of documentation is less than ideal.
Weierstrass semigroup at $$m+1$$ rational points in maximal curves which cannot be covered by the Hermitian curve

We determine the Weierstrass semigroup $H(P_\infty,P_1,\ldots,P_m)$ at several rational points on the maximal curves which cannot be covered by the Hermitian curve introduced by Tafazolian, Teher\'an-Herrera, and Torres. Furthermore, we present some conditions to find pure gaps. We use this semigroup to obtain AG codes with better relative parameters than comparable one-point AG codes arising from these curves.
Generalized Organ Segmentation by Imitating One-Shot Reasoning Using Anatomical Correlation

Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human’s computational neural system. In medical image analysis, given several exemplars (anchors), experienced radiologist has the ability to delineate unfamiliar organs by imitating the reasoning process learned from existing types of organs. Inspired by this observation, we propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic. We propose pyramid reasoning modules (PRMs) to model the anatomical correlation between anchor and target volumes. In practice, the proposed module first computes a correlation matrix between target and anchor computerized tomography (CT) volumes. Then, this matrix is used to transform the feature representations of both anchor volume and its segmentation mask. Finally, OrganNet learns to fuse the representations from various inputs and predicts segmentation results for target volume. Extensive experiments show that OrganNet can effectively resist the wide variations in organ morphology and produce state-of-the-art results in one-shot segmentation task. Moreover, even when compared with fully-supervised segmentation models, OrganNet is still able to produce satisfying segmentation results.