Reviewerly/exHarmony huggingface dataset:
Application of Data Mining With Naïve Bayes Classifier Algorithm to Predict Customer Character

This research is based on late payment data from UD Delta customers which affect all production processes that occur in UD Delta. The purpose of this study is to predict the character of the customer as a basis for analyzing the delay in the payment process that hinders the company's production. The datasets tested were 30% of the 600 data, which were divided into 1 id, 7 attributes and for the prediction classes 'No Problem', 'Slightly Problematic', and 'Very Problematic'. This study collects data by means of non-participant observation, namely data on UD Delta's bookkeeping. The process stage is processed using the python programming language with classification methods and naive bayes algorithms. The results obtained from this study are in the form of calculations that state the number of customers grouped into 3 categories, namely with correct predictions as much as 51%, incorrect predictions as much as 49%. From the results obtained, it will be taken into consideration by the company in processing the existing processes within the company as a step to develop the business carried out so that it is more optimal.
The Method for Plausibility Evaluation of Knowledge Triple Based on QA

At present, most of the methods for knowledge graph completion (KGC) task highly rely on external knowledge base or graph representation learning. However, how to complete this task without using any external prior knowledge is still a huge challenge and difficulty. To this end, we propose a novel framework which converts the plausibility evaluation of knowledge triple task to the question and answer (QA) task with the thought of KG-BERT and prompt learning. We also test the effect of different question types on the results. Secondly, by fine-tuning two pre-trained language models BERT-wwm-ext and ERNIE-Gram on these generated sequences, so that they can complete the QA task. We won the 5th place at CCKS 2022 track 1 rematch stage, which proved the effectiveness of our method.
THE CORRELATION OF CULTURE AND EDUCATION IN THE PHILOSOPHY OF S. I. HESSEN

The Russian philosopher, who lived for a long time in exile since 1922, Sergey Iosifovich Gessen is known to a wide range of researchers as a scientist who analyzed the fundamental problems of educational action based on the principles of humanistic dialogue, and also looked for ways and means of acquiring pedagogical skills consisting in sensitive care and a deep understanding of the process of personality formation. An important section of S. I. Hessen’s philosophical concept of education is the theory of scientific education. Based on the achievements of classical philosophical thought presented in various directions and currents, the philosopher develops critical didactics, the mechanisms of which, according to the thinker, should be actively and consistently introduced into the pedagogical process.
Identification of Transfer Assistance Tasks in Nursing Homes Using Wearable Embedded Sensors

In the field of nursing care, the burden of many recording tasks, including nursing care records, has become a problem. In order to optimize the division of work and to reduce the burden of indirect work, it is necessary to have a system that can automatically record the contents of work at the actual work site. In this study, we use inertial sensors and plantar pressure sensors attached only to the back, waist, and shoes to measure movements and recognize transfer assistance tasks.
Moving+: Semantic Scene Classification on YOLOv5

For intelligent moving agents, robots especially, it is important to be aware of the surroundings to help analyze the situation and what might happen in the future. Scene classification is a hotspot with the development of moving agent. Different from the approach directly solving the problem caused by moving platform, our approach does the classification on object detection results from YOLOv5, which is with meaningful and semantic information. Since YOLOv5 works on frames in video, with state of art detect speed and accuracy in area of object detection, it can perfectly avoid performance degradation caused by the moving platform. By further integrating with TF-IDF, five ways to train the model are obtained, the semantic representation sequence is feed into LSTM to handle the temporal relations among frames. Our dataset was consisted with three parts: moving+ dataset: taken from a mobile robot platform, extension dataset: downloaded from internet by keywords retrieval and mixed dataset: mix both of them. Experiment results on three datasets prove the effectiveness of our approach particularly in the moving+ dataset and mixed dataset, our approach shows a high recognize accuracy up to 93% and 92%.
Proposal of Event-Driven Particle Filter for Object Tracking

A method is proposed for tracking pulsating objects by integrating a high-speed, low-latency event-driven camera and a newly developed event-driven particle filter. The event-driven particle filter is an efficient algorithm that computes the input from an event-driven camera as an event representation. It updates the particle state by a state transition model and the particle weight by a likelihood calculation for each event occurrence. The proposed method was evaluated by presenting an event-driven camera with a test video of a target in pulsating motion on a fast-refresh-rate display. The experimental results confirm that the method can estimate the next state in a 1-ms period of the target.
The Luxembourg National Research Fund and ChronosHub: Lessons learned from implementing an Open Access Management platform

Luxembourg is well-known for its continuous efforts to develop a world-class research and innovation landscape and often ranks 1st for ‘Attractive research systems’ [1] on the European Innovation Scoreboard. In this context, the Luxembourg National Research Fund (FNR) plays an important role and introduced its first open access policy in 2017 to ensure that all publications from its funding becomes freely accessible. The FNR also set up an open access fund to cover Article Processing Charges (APC) for these publications. To implement the policy and make sure its funding eligibility criteria were met, data had to be collected and checked manually for each payment request, which involved a high volume of administrative work for both grantees and administrators. Prompted to find a solution to address these challenges, FNR and the Luxembourg National Library (BnL) started a pilot in 2020 together with ChronosHub, an open access management platform. By mid-2021, the pilot phase ended and FNR, BnL and ChronosHub transitioned into long-term operation to streamline open access management on a national level, tying in with all research institutions in the country. Subsequently, all APC payments related to FNR grants are managed through the ChronosHub platform. Authors can do compliance checks for each journal before submission, and upon article acceptance the data ingestion, funding eligibility checks, APC payments and reporting are now more automated. As a result, more authors benefit from getting their APCs funded. However, there are still challenges causing manual work, especially for APCs beyond FNR's funding cap as the institutions, in such cases, need to cover one part of the APC. This paper is a write up of the presentation given during the CRIS2022 conference in May 2022, giving an overview of the initiative, and its problems, solutions, lessons learned and remaining challenges.
IP Security (IPsec)

The network layer 3 of the ISO/OSI model transmits data packets over long distances and different layer 2 technologies. In TCP/IP networks, there is only one protocol at the network layer: the Internet Protocol (IP; Figure 8.1). Adding cryptographic security at this layer seems a natural choice to protect all Internet traffic. There is no reliable data transfer at the Internet layer, and IP packets may arrive out of order. These characteristic features, which we summarize at the beginning of this chapter, had to be considered when applying cryptographic algorithms to IP packets. SKIP, a first attempt to secure the Internet layer by Sun Microsystems 8.2 was later abandoned in favor of the IETF IPsec protocol suite described in sections 8.3 to 8.5. IPsec is an example of a cryptographic protocol suite that was standardized from scratch and was criticized as being overly complex [22]. We try to tame this complexity, especially in the first version of the Internet Key Exchange protocol, by identifying similarities and differences between the many options. Additionally, we give some historical background on the development of IKEv1 to better understand the different components of the protocol. IPsec is available in all major operating systems and many network appliances. Today, IPsec is the most crucial technology for implementing virtual private networks. Academic research on IPsec is sparse, which may be related to the protocol’s complexity.
(Pre-print) The intersections between Artificial Intelligence, Intellectual Property, and the Sustainable Development Goals

The Sustainable Development Goals (SDGs) represent the main hope for peace and prosperity in the near future, according to the 2030 UN Agenda. Artificial Intelligence (AI) is a significant technological advancement of the Fourth Industrial Revolution. Intellectual Property (IP) is the system that incentivises innovation worldwide. These three areas influence each other. This chapter aims to illustrate the intersections between AI, IP, and SDGs that emerge from the literature and can be relevant from a policy perspective. The objective is to unveil research areas that can advance scientific understanding of how IP contributes to the SDGs, by using and incentivising AI methods to inform IP offices, businesses, and policymakers. The chapter concludes by providing a line of research of that kind.
Positive Unlabeled Learning by Sample Selection and Prototype Refinement

Positive and Unlabeled learning (PU learning) learns a binary classifier on training data with only positive and unlabeled instances. Recent cost-sensitive methods tackled this problem by designing unbiased loss functions and achieved state-of-the-art performance. However, we observe that the model suffers from overfitting at the late training stage caused by regarding unlabeled positive samples as negative ones. This motivates us to propose PUSP, a novel framework that leverages sample selection and prototype refinement to tackle PU learning problem. We first carefully select reliable samples based on the time consistency of the model output and spatial consistency of different views of image contents. Then we assign labels to those unselected samples based on their feature similarities with the prototypes of the selected ones. We conduct extensive experiments to show the effectiveness of our method on over different datasets and modalities.
Pengembangan Handout Materi Bangun Ruang Sisi Datar Kelas VIII Berdasarkan Pendekatan Pembelajaran Matematika Realistik

Learning resources in the form of textbooks and LKS often make students feel bored during learning activities, because these two learning sources often only contain formulas and material, without any comprehensive explanation of mathematical concepts. Therefore, there is a need for innovation in the learning resources used in schools. This research is aimed at developing handouts for class VIII on flat sided of three-dimensional material based on the Realistic Mathematics Learning approach. This type of research is research and development (Research and Development) with the 4-D model development method by Thiagarajan, namely: 1) Define, 2) Design, 3) Develop, and 4) Disseminate. The results of the material expert's validation of the handout showed an average value of 0.8106 with high validity criteria and was declared feasible. The results of the media expert's validation of the handout showed an average value of 0.8698 with high validity criteria and was declared feasible. From the post-test results of the experimental class, it was found that almost all students passed. The results of the Independent Sample T-Test at the testing stage for the use of the experimental class showed a value of 0.034, thus a significance value of &lt;0.05 was obtained, which means that there was a significant difference in improving student learning outcomes between the experimental class and the control class. This is in line with the students' responses at the usage trial stage obtaining a percentage of 79% with the criteria that almost all students gave a positive response to the handout.
Development and characterisation of CRIS systems in Latin America: Preliminary results of diagnostic survey

In order to carry out a diagnostic study on the development of CRIS systems in Latin America, an online survey was implemented from July to November 2021, targeting higher education and research institutions in 21 countries in the region. A total of 133 institutions from 16 countries responded to the call. Among the results, 23% (31 institutions) responded that they do have a CRIS system in place. The next phase of the diagnostic study will consist of documenting the national CRIS projects found in 7 countries in the region.
Discrimination of Structures in a Nuclear Facility by Deep Learning Based on 3D Point Cloud

This paper describes a method for discrimination of structures in a nuclear facility by deep learning based on projected images generated from a 3D point cloud using a virtual camera. In order to create a training dataset, we constructed a system that automatically generates both projected and labeled images from 3D point cloud data along a pre-defined trajectory path using a virtual camera. Finally, we reported the training accuracy and discrimination performance by the training dataset created by the proposed method from the point cloud measured at the test plant in the Fukushima Robot Test Field.
Effective Segmentation of RSSI Timeseries Produced by Stationary IoT Nodes: Comparative Study

The Received Signal Strength Indicator (RSSI) timeseries have been used as a primary variable in many cybersecurity applications, such as wireless-node profiling for the purpose of authentication, localization, and physical security perimeter monitoring. Previous research on the use of RSSI-based wireless node profiling assumes that RSSI timeseries are stationary and independent identically distributed (i.i.d.). Unfortunately, in real-world environments, this assumption is far from the truth and would negatively impact the performance of any system or application built on idealized models of RSSI timeseries data. In other words, a set of real-world RSSI values (depending on the variability of noise produced by objects in the environment) are typically made of sub-segments each with its own statistical characteristics (e.g., mean and variance). Therefore, before any modelling attempt, one must consider breaking down a given RSSI dataset into its constituting sub-segments. Unfortunately, the effect of environmental variables on RSSI values tend to be random, which makes the problem of RSSI timeseries segmentation even more challenging. Thus, it is necessary to study the effectiveness of existing notable timeseries segmentation algorithms against a dataset of RSSI values. The main contributions of our work are that (1) we have demonstrated the non-stationary nature of RSSI timeseries by collecting samples from a real-world IoT network, and (2) through real-world experimentation we have compared the effectiveness of notable timeseries segmentation methods for the discovery of sub-segments in a RSSI timeseries dataset. Our work highlights the importance of accurate detection of change points in RSSI timeseries, which can further facilitate optimal selection and performance of the respective system’s cost and objective functions. Finally, we demonstrate that the $$\ell _1$$ cost function can capture a meaningful relationship between neighboring data points in a RSSI timeseries and can result in a stable segmentation across different search methods.
Regularized Framework on Heterogeneous Hypergraph Model for Personal Recommendation

Filtering-based recommendations are one of the most widely used recommendation algorithms in recent years. Most of which are based on simple graph to construct network model, and the nodes connected by edges are all pairwise relationships. In practice, some relationships are more complex than pairwise relationships. Moreover, collaborative filtering only focused on the relationships between users and users, items and items, users and items, without considering the relations between items and tags. To address these problems and improve the accuracy of the recommendation algorithms, we propose a regularized framework based on heterogeneous hypergraph, which integrates tag information into the recommendation system. Firstly, the hyperedges are built for each user and all items those are rated by the user, and then the similarity index between items in the hyperedge is calculated. Secondly, the relational graph between items and tags is constructed. Thirdly, we establish a regularization framework, and minimize the cost function for scoring prediction and recommendation. Finally, we verify the effectiveness of our proposed algorithm on Movielens-100k, Restaurant & consumer and Filmtrust datasets, and the diverse simulation results show that our proposed algorithm gains better recommendation performance.
Smartphone-Based Input Multimodal Interactions for IoT Environments

Multimodal interfaces offer users different and appropriate ways to communicate with smart devices. However, despite the advantages of multimodal interactions and their interest in connected environments, only a few of them could reach end users. One of the problems causing this concerns the obligation of using intermediate equipment between users and the connected objects (cameras, Kinect, smart-watch, etc.). Equipment that is not necessarily available for most users and even if they exist, can represent a disadvantage in everyday life. The novelty of our approach is proving that the different intermediate equipment/sensors used in the literature between the user and connected objects can be replaced by the smartphone. An equipment that not only contains an important number of sensors, but also exists in most people’s homes. Our approach seeks, therefore, to use smartphones instead of different equipment in the literature studies that use intermediate equipment to ensure multimodal interactions with connected objects. We are starting by transforming the well-known interaction modality “Smart Pockets”, into a smartphone-based interaction modality. Then, we will follow the same process with other modalities and/or other connected objects and we will evaluate users’ feedback. We believe that this will prove the feasibility and allow, afterwards, a democratization of smartphone-based multimodal interactions by making it available to all owners of smartphones and connected objects.
Improved Differential Attack on Round-Reduced LEA

LEA is both the national standard of the Republic of Korea and an ISO/IEC standard. In this paper, we focus on differential attack on LEA with the automatic analysis technique and improve the previous searching strategy in ARX ciphers. For new strategy, we no longer just pay attention to the internal difference with only one active bit. By studying the differential property of modular addition, we choose the proper difference in the middle round. We construct a 13-round differential characteristic whose probability is better than the best previous one with a factor of about 2. Furthermore, We take the differential effect into consideration and obtain a 13-round differential whose probability is better than the best previous one with a factor of about 4. Moreover, we mount key-recovery on 14-round LEA-128, 14-round LEA-192 and 15-round LEA-256. Utilizing the property for key schedule, we obtain the lower time complexity than that evaluated by Dinur’s method.
КОНЦЕПЦИЯ «НАДЗИРАЮЩЕГО КАПИТАЛИЗМА» И СТРАТЕГИЧЕСКИЕ ЗАДАЧИ СОВРЕМЕННОГО РОССИЙСКОГО ОБРАЗОВАНИЯ

The article considers the question of the optimal construction of a strategy for the development of the education system in modern Russia, taking into account its internal problems and the global transformations taking place in the world.
String Handling

In the previous chapter, we gained knowledge of interfaces and abstract classes and their relationship with classes and objects. We saw that interfaces and abstract classes can be used to make classes have a consistency when they inherit from the interface or abstract class. We also read that many developers will program to an interface, showing that interfaces form an integral part of developing C# applications in a commercial environment.
INTERDISCIPLINARY LINKS IN TRAINING OF COMPUTER MODELING IN 5T GRADE

The goals and objectives of computer modelling training are aimed at building digital competencies of the student and their application in various subject areas. The report presents an approach to building of interdisciplinary links in computer modelling training through a block programming language. The constructive approach and the easy visualization of the various options for solutions make it possible to make a propaedeutic of still unexplored knowledge of mathematics, physics, astronomy and others. Working on projects that solve certain practical problems gives students the opportunity to get acquainted with elements of combinatory, optimization, the laws of uniform motion, space objects, number theory and more. The authors will share their experience in developing exemplary interdisciplinary assignments.
Research on Named Entity Recognition Technology based on pre-trained model

The pretraining model has the problems of large number of parameters, complex structure, and difficult to be compressed into a small model with both performance in practical deployment. Aiming at the above problems, this paper proposes a lightweight entity recognition model with good performance by using the framework of knowledge distillation to supervise and guide the student entity recognition model through the teacher entity recognition model. Finally, the feasibility and effectiveness of the method proposed in this chapter are verified by experiments.
THE ENLIGHTENMENT PHILOSOPHY OF CULTURE BY J. J. ROUSSEAU

The article deals with the educational philosophy of culture of J. J. Rousseau. Enlightenment philosophers advocated the improvement of society and man under the slogans of the triumph of science and progress. They stood up for the dissemination of knowledge, for the thoughtful education of a person who, as they believed, is by nature good and his vices are due to the vices of society. The main acquisition of the Enlightenment of the 18th century in the field of philosophical understanding of culture was the promotion of its historical parameters, which meant significant shifts in the problems inherited from its predecessors. Jean-Jacques Rousseau’s theory of civilization occupies a special place in the philosophy of the Enlightenment. In his theoretical heritage, the question of the fate of civilization has become one of the central ones. Rousseau was the first of the modern thinkers who formulated a conclusion about the historical content of the essence of man.