Reviewerly/exHarmony huggingface dataset:
A Quantum Algorithm to Locate Unknown Hashgrams

Quantum computing has evolved quickly in recent years and is showing significant benefits in a variety of fields, especially in the realm of cybersecurity. The combination of software used to locate the most frequent hashes and n-grams that identify malicious software could greatly benefit from a quantum algorithm. By loading the table of hashes and n-grams into a quantum computer we can speed up the process of mapping n-grams to their hashes. The first phase will be to use KiloGram to find the top-k hashes and n-grams for a large malware corpus. From here, the resulting hash table is then loaded into a quantum simulator. A quantum search algorithm is then used search among every permutation of the entangled key and value pairs to find the desired hash value. This prevents one from having to re-compute hashes for a set of n-grams, which can take on average O(MN) time, whereas the quantum algorithm could take $$O(\sqrt{N})$$ in the number of table lookups to find the desired hash values.
Obstacle Recognition Using Depth Estimation and RGB Data for Autonomous Vehicle

In the past few years, autonomous vehicles have been the subject of innovation and technology amidst the challenges and difficult situations surrounding the urban driving and infrastructure. The recent developments in the sensors and embedded systems made way to do a deeper analysis of the cost and needs of AV. This study aims to make use of these new technologies and sensor to have a better recognition model of the relevant road actors surrounding autonomous vehicles. The results of this study made it possible to use depth and RGB data to recognize obstacles in an urban road driving scenario. The mean average precision of the model across all labels shows acceptable results running in ten frames per second setting. The model was deployed in an autonomous golf picker buggy using Robotic Operating System.
Trend of mobile, web, E-learning research in 2002-2021: Contribution to physics learning

The existence of a research topic can be seen from the research trend. Analysis that can map data to determine the trend of a study is using bibliometrics. The purpose of this study is to compare trends of mobile, web, and e-learning research and their contribution to physics learning in the last twenty years. The data was obtained from Scopus which was processed using Ms. Excel and VOSviewer. The three topics show a steady increasing trend in the last three years. The highest average citation per paper per year is in 2020. The most widely used keywords are mobile learning, web-based learning and e-learning. The contribution of the three topics to physics learning includes increasing motivation, learning outcomes, etc. Future research can be carried out to compare the contribution of the three topics to other learning systems such as STEM, STEAM, etc. using databases other than Scopus. Keywords: Bibliometrics; E-Learning; Mobile Learning; Physics Learning; Web Learning.
What Is the Value of a Person When Artificial Intelligence Can Do All the Work?

In this chapter, we challenge the claim put forward by our students that people have no value if machines can do all the jobs currently performed by humans. We begin by showing that, given certain problems of energy use, creativity, and possible non-material components (either as a “soul” or via quantum mechanics), it is not likely that machines will ever be able to do everything that humans do. Then, we show that, for reasons of both ability (which could possibly change, given advances in AI technology, but seem unlikely to change at this time) and ontology (i.e., there are some things that only a human can do for us), it is not desirable for machines to do all jobs. Finally, we argue that, even if machines could do all the jobs that humans currently do, this should not affect the value of human beings, since our value as humans does not come from our economic productivity but is inherent to human being itself.
A Deep Dynamic Latent Block Model for the Co-Clustering of Zero-Inflated Data Matrices

The simultaneous clustering of observations and features of data sets (a.k.a. co-clustering) has recently emerged as a central machine learning task to summarize massive data sets. However, most existing models focus on stationary scenarios, where cluster assignments do not evolve in time. This work introduces a novel latent block model for the dynamic co-clustering of data matrices with high sparsity. The data are assumed to follow dynamic mixtures of block-dependent zero-inflated distributions. Moreover, the sparsity parameter as well as the cluster proportions are assumed to be driven by dynamic systems, whose parameters must be estimated. The inference of the model parameters relies on an original variational EM algorithm whose maximization step trains fully connected neural networks that approximate the dynamic systems. Due to the model ability to work with empty clusters, the selection of the number of clusters can be done in a (computationally) parsimonious way. Numerical experiments on simulated and real world data sets demonstrate the effectiveness of the proposed methodology in the context of count data.
Knowledge about the recent past affects human gaze patterns

Humans move their eyes to explore their surroundings, constantly changing the visual input they receive. Usually, the current visual input follows naturally from immediately preceding input. This temporal continuity of information enables the accumulation of knowledge about the environment. Here, we investigated how recently-gained knowledge influences gaze patterns and their underlying information-gathering strategy. Using eye tracking, we examined if people looked differently at natural scenes, depending on the presence or absence of relevant knowledge about the events immediately preceding the scenes. 48 Participants viewed 80 sequences of movie frames that comprised several ‘context’ frames showing an unfolding series of events, followed by a ‘critical’ frame. In an Expected condition, critical frames depicted natural continuations of events presented in the context frames. In an Unexpected condition, the context frames were unrelated to the critical frame. Importantly, critical frames were identical in both experimental conditions; only the context frames differed. We measured characteristics of gaze-patterns registered on the critical frames. Linear mixed-effects models analysis revealed that in the Unexpected condition, in comparison to the Expected, two measures increased (number of fixations and saccade length), two decreased (fixation duration and number of blinks; all p-values < 0.001), and one remained unaffected (first-saccade latency). These results indicate that without relevant knowledge about past events, oculomotor behavior is more exploratory. Next, we tested which information best differentiates the conditions. We fitted models which separate ‘spatial’ and ‘temporal’ information (hidden Markov models) to scan-paths from critical frames and used a machine-learning technique (linear discriminant analysis) to classify these models into two conditions. The classifier achieved 69% accuracy (chance-level: 50%). Crucially, discarding spatial, but not temporal, model-components substantially decreased the classifier’s performance, demonstrating that the conditions differ predominantly regarding sampled image-regions. In summary, we demonstrate that eye movements are influenced by information about the recent past.
An Efficient Text Detection and Recognition Framework for Natural Scene Images

For understanding the content of the image, a vital clue is the superimposed text in images. However, ineffective text extraction might be caused by the poor contrast and complicated background of the image. An effective text detection and recognition framework as of natural scene images utilizing Anopheles search with convolution neural network are proposed to mitigate such complexities. In this work, for detecting and recognizing the exact text from the image, different steps are undergone by the input images from the street view text (SVT) dataset. The precise text is extracted from the images. For comparing the results attained by the proposed AS-CNN with the preceding top-notch algorithms, SVT and publically accessible datasets are utilized in this research. The experimental outcomes denoted that the proposed AS-CNN exhibits promising results, effectively decreasing the overall computation cost and time.
Hybrid Malicious Encrypted Network Traffic Flow Detection Model

Encrypted communication technology has evolved as the network, and Internet applications have advanced. Malicious communication, on the other hand, employs encryption to bypass standard detection and security protection. The existing security prevention and detection technologies are unable to identify harmful communication that is encrypted. The growth of artificial intelligence (AI) in these days has enabled to employ machine learning (ML) as well as deep learning approaches to identify encrypted malicious communications without decryption, with remarkably precise detection outcomes. At this moment, research on detecting harmful encrypted traffic is mostly focused on analyzing the features of encrypted data and selecting neural network (NN) techniques. Hybrid ML is proposed in this study by merging two well-performing data mining algorithms with natural language processing tasks. Here, a new traffic flow detection method is performed by the hybrid ML technique. At first, the benchmark data is collected from public sources. The features are extracted using the convolutional layer of deep convolutional neural network (DCNN). Then, the weighted feature extraction is performed by grasshopper optimization algorithm (GOA). Employed the hybrid machine learning-based malicious detection with the “support vector machine (SVM) and neural network (NN)” is utilized in this model to detect the traffic affected by malicious activities, where the hidden neuron count of NN and kernel of SVM are tuning by the same GOA for increasing the accuracy and precision. This research provides findings from experiment, encouraging various researchers to develop the research as future work.
Study on Ways for Restructuring the National Curriculum Development System: Following the Launch of the National Board of Education

Purpose: This study examined ‘ways’ of restructuring the national curriculum development system after the launch of the National Board of Education, by comparing and analyzing both of the procedures in the ‘2015 Revised Curriculum’ and the new national curriculum predicted by the enforcement of the ‘National Board of Education Act’.&#x0D; Methods: This study comprehensively analyzed the‘National Board of Education Act’ with its implementing ordinances and related research reports.&#x0D; Results: The study restructured the steps as [initiation-R&amp;D-proposal review-notification-evaluation]for revising the national curriculum, which should be carried out by the National Board of Education and proposed ‘student/faculty/general citizen participation’, ‘evaluation-based feedback circulation’ and‘cooperation for learner growth support’ as the ways for the national curriculum development system.&#x0D; Conclusion: It was confirmed that this structure was required where the various subjects involved participated in each revision step directly or indirectly, in order to solve various problems, discerned thtough the educational perspectives, raised during the revision process of the national curriculum.
Demonstrating hitonavi- <i>μ</i>

In this paper, we demonstrate a brand new design of a wearable device that enables privacy-preserving human activity recognition based on light-weight compact-size LiDAR. The device scans and represents the surrounding environment in 3D point clouds form. The system further processes this representation to define discriminative features that facilitate recognizing human activity on edge. These features are extracted using Spatio-temporal probabilistic clustering and fisher vector representations and then used to train a classification model for activity recognition purposes. Implementation and evaluation of the proposed system confirm its efficient ability to identify human activities.
Development of training programs and evaluation methods for question intelligence

Asking questions is key to advancing knowledge and asking the creative questions is especially important. Being able to properly frame and focus on the creative questions is an important skill but this isn’t something that is taught or evaluated within educational systems. Professor Fumihito Ikeda, Brain Science Research and Education Center, Institution for the Advancement of Higher Education, Hokkaido University, Japan, believes that nurturing children’s development of creative questioning skills at school would be beneficial for the leaders of tomorrow. He and his team are developing test questions and training programmes for schools that generate questions focused on three types of logical reasoning: inductive, deductive and abductive. Ikeda’s goal is to develop and train mainly primary and high school students in question-intelligence. A test question using the three types of reasoning was created and tried on around 200 high school students. The researchers utilised, a deep neural network (DNN), a form of machine learning, to assist in the automatic classification and evaluation of questions.Ikeda plans to create an inquiry learning programme that will improve the quality of scientific questions while asking different types of logic in order to collect more appropriate questions for DNN to learn. He will use his partnerships with the Japan Society for Science Education and the Japan Creativity Society to roll these programmes out to students at educational institutions.
KiCi

Wearable-based human activity recognition (HAR) is commonly employed in real-world scenarios such as health monitoring, auxiliary diagnosis, etc. As implementing activity recognition is a daunting challenge in an open dynamic environment, incremental learning has become a common method to adapt to variable behavior patterns of users and create dynamic modeling in activity recognition. However, catastrophic forgetting is a significant challenge with incremental learning. This is contrary to our expectations of identifying new activity classes while remembering existing ones. To address this problem, we propose a knowledge importance-based class incremental learning method called KiCi and construct an incremental learning model based on the framework of self-iterative knowledge distillation for dynamic activity recognition. To eliminate the prediction bias of the teacher model on the old knowledge, we utilize the trained weights of previous incremental steps generated by the teacher model as the prior knowledge to obtain knowledge importance. Then use it to make the student model have a reasonable trade-off between old and new knowledge and mitigate catastrophic forgetting by avoiding negative transfer. We conduct extensive experiments on four public HAR datasets and our method consistently outperforms the existing state-of-the-art methods by a large margin.
CStory: A Chinese Large-scale News Storyline Dataset

In today's massive news streams, storylines can help us discover related event pairs and understand the evolution of hot events. Hence many efforts have been devoted to automatically constructing news storylines. However, the development of these methods is strongly limited by the size and quality of existing storyline datasets since news storylines are expensive to annotate as they contain a myriad of unlabeled relationships growing quadratically with the number of news events. Working around these difficulties, we propose a sophisticated pre-processing method to filter candidate news pairs by entity co-occurrence and semantic similarity. With the filter reducing annotation overhead, we construct CStory, a large-scale Chinese news storyline dataset, which contains 11,978 news articles, 112,549 manually labeled storyline relation pairs, and 49,832 evidence sentences for annotation judgment. We conduct extensive experiments on CStory using various algorithms and find that constructing news storylines is challenging even for pre-trained language models. Empirical analysis shows that the sample unbalance issue significantly influences model performance, which shall be the focus of future works. Our dataset is now publicly available at https://github.com/THU-KEG/CStory.
Auto-Tuning PI Controller Design Using Fuzzy Logic Controller for Web Tension Control in Roll Rewinder System

Paper has a very important role, because it increases the productivity of pulp and paper companies in Indonesia. The company's development in 2018 increased by 2.1%, this was indicated by increasing consumer demand. Therefore, paper becomes a necessity in everyday life. The criteria for the paper to be produced are paper with a flat surface and cannot be wrinkled. However, errors in paper production that often occur are a lot of wrinkles and uneven paper surfaces. This happens because of a discrepancy in the dynamics of paper tension with the standards that have been set. The paper record is caused by the dynamics of the web tension in the Rewinder Roll system. In order for the web tension to remain stable, a controller is needed. The controller used is Auto-Tuning PI controller using Fuzzy Logic Controller (FLC). The controller can be said to be successful when it is able to perform automatic tuning of setpoint changes. The results showed that the system was able to achieve setpoint values, overcome overshoot and steady-state error (Ess) with response time values, namely Tr=0.3151 seconds, Ts=0.3750, Td=0.0721, overshoot=0.0044%, and Ess=0 N.
Natural Post-Disaster Management Archive Model

As a country within the "ring of fire" geographical area, Indonesia is vulnerable to natural disasters. The impact of natural disasters is not only physical and human, but also relates to the records that are owned. This study strives to support efforts to save archives after natural disasters and develop a model for saving and restoring archives after natural disasters happens. The results of the research showed that there is no a standardized procedures to save and restore archive in times of disasters, therefore there shall be regulation to make the regional disaster agency, the regency offices, and library and archives agency coordinate and cooperate in a single task-force, based on our developed model.Keywords: disaster; archives; archive recovery
Study on A Mechanical Data Glove

Visual recognition, point tracking recognition and micro-sensor technology can be used to monitor the hand posture, but they all have some shortcomings, such as expensive system, complex algorithm and difficult processing. To solve these problems, this paper designs a mechanical data glove based on variable resistor. The finger bending information is collected through the relationship between the output of potentiometers and hand posture, and the finger bending information is converted into digital signal, which can be displayed again after processing. The data glove not only has simple structure and low cost, but also can effectively monitor the hand posture.
Digital innovations through the prism of perception by Belarusian students

In the sphere of higher education the imposed digitalisation of learning processes under conditions of the pandemic has made distance forms of education a reality for most students and therefore helped them increase their level of knowledge of information and communication technologies and competences. Digital innovations have become the key features of an image of a contemporary society that most students constructed in their consciousness. However, virtual reality differs from the real everyday life that is not necessarily technologically advanced. This contradiction can be an important reason for several risks and illusions emerging in the students’ perception of digital transformation and overpriced expectations from the processes of digitalisation at work, study and everyday life. Depending on the type of students’ assessments of the role of digital innovations in a society, five theoretical patterns of students’ attitudes towards digital innovation have been selected. The results from this research indicate the importance of the university learning environment to discuss with the students the real benefits and potential threats of digital innovations.
Anonymous blind quantum computation protocol fordistributed quantum networks

<p indent="0mm">Quantum cyber security aims to examine the communication and computation of security within a distributed quantum network. Two primary security concerns, privacy and anonymity, are typically investigated in quantum computational tasks. Therefore, this study proposes a state-of-the-art, single-server, multi-user anonymous blind quantum computation protocol, wherein the anonymous entanglement shared between the server and user is utilized as a quantum resource for blind quantum computation tasks. The anonymity, blindness, and security analyses demonstrated that the proposed protocol can perform blind quantum computation tasks while maintaining the confidentiality of the user identity. Compared with conventional blind quantum computation protocols, this protocol provides additional security for blind quantum computing and serves as a novel design idea for anonymous quantum cryptography protocols.
Cloud service checklist for academic communities and customization for genome medical research

Abstract In this paper, we present a cloud service checklist designed to help IT administrators or researchers in academic organizations select the most suitable cloud services. This checklist, which comprises items that we believe IT administrators or researchers in academic organizations should consider when they adopt cloud services, comprehensively covers the issues related to a variety of cloud services, including security, functionality, performance, and law. In response to the increasing demands for storage and computing resources in genome medical science communities, various guidelines for using resources operated by external organizations, such as cloud services, have been published by different academic funding agencies and the Japanese government. However, it is sometimes difficult to identify the checklist items that satisfy the genome medical science community’s guidelines, and some of these requirements are not included in the existing checklists. This issue provided our motivation for creating a cloud service checklist customized for genome medical research communities. The resulting customized checklist is designed to help researchers easily find information about the cloud services that satisfy the guidelines in genome medical science communities. Additionally, we explore whether many cloud service providers satisfy the requirements or checklist items in the cloud service checklist for genome medical research by evaluating their survey responses.
Prediction of Surface Roughness of U71Mn Steel Milling Based on RBF Neural Network

In order to predict the surface roughness of U71Mn high manganese steel before actual milling operation, an orthogonal experiment was designed. Based on the intelligent algorithm of Radial Basis Functions (RBF) neural network, an accurate prediction model of surface roughness is done with MATLAB. By comparing the predicted data of RBF neural network model with the actual measured data, it is proven that the model is accurate and effective.