Reviewerly/exHarmony huggingface dataset:
Face Recognition System Using Independent Components Analysis and Support Vector Neural Network Classifier

With an increasing number of security threats in recent years, the field of automatic facial recognition has seen many new developments. The introduction of many new face recognition algorithms focuses on increasing the accuracy rate of the recognition system. This paper introduces a face recognition system using Independent Component Analysis (lCA) for feature extraction and a Support Vector Neural Network (SVNN) for classification. As well as introducing a comparison between SVNN and Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers, they are applied to prove the reliability of the proposed method. The implemented experiments use Yale databases, and the results prove that the proposed approach has a higher recognition rate than the (ICA+SVM) and (ICA+ANN) approaches for face recognition.
Blockchain: A Makeover to Supply Chain Management

Blockchain, the distributed ledger technology with phenomenal capabilities, is gaining the attention of supply chain managers. This paper is all about how blockchain renovates the current supply chain management practices. It discusses the major shortcomings of existing supply chains and the ways blockchain halts them. Shortcomings are identified from the literature and mapping is done with the characteristics of blockchain technology with proper justification. Blockchain has numerous possibilities in the domain of supply chain management and this paper presents them from different perspectives. Based on the evidences from the literature, this paper investigates how blockchain can influence the flow of money, material and information in a supply chain along with important applications. The possible combinations of blockchain with other technologies are identified and their scope in supply chain management is briefly discussed. Ultimately, this paper lists out the core attributes of blockchain-enabled supply chains. A comprehensive view of some of the real-world use-cases is also presented in this paper. In short, this paper provides a short reference for the readers, to understand the way how blockchain is being a makeover to supply chain management.
A Note on the Post-quantum Security of (Ring) Signatures

This work revisits the security of classical signatures and ring signatures in a quantum world. For (ordinary) signatures, we focus on the arguably preferable security notion of blind-unforgeability recently proposed by Alagic et al. (Eurocrypt'20). We present two short signature schemes achieving this notion: one is in the quantum random oracle model, assuming quantum hardness of SIS; and the other is in the plain model, assuming quantum hardness of LWE with super-polynomial modulus. Prior to this work, the only known blind-unforgeable schemes are Lamport's one-time signature and the Winternitz one-time signature, and both of them are in the quantum random oracle model. For ring signatures, the recent work by Chatterjee et al. (Crypto'21) proposes a definition trying to capture adversaries with quantum access to the signer. However, it is unclear if their definition, when restricted to the classical world, is as strong as the standard security notion for ring signatures. They also present a construction that only partially achieves (even) this seeming weak definition, in the sense that the adversary can only conduct superposition attacks over the messages, but not the rings. We propose a new definition that does not suffer from the above issue. Our definition is an analog to the blind-unforgeability in the ring signature setting. Moreover, assuming the quantum hardness of LWE, we construct a compiler converting any blind-unforgeable (ordinary) signatures to a ring signature satisfying our definition.
PG-explorer: Resource Description Framework data exploration with property graphs

The Resource Description Framework (RDF) has been widely applied to represent and exchange domain information because of its machine-readable characteristic. With a huge amount of RDF data available, retrieving RDF data is essential, so that many RDF query approaches have been developed. But many traditional approaches generally require users to know RDF model and query language, and this seriously prevents a large number of common non-expert users from obtaining information in RDF datasets. In this paper, we propose an approach that users can explore massive RDF datasets by interactively manipulating property graphs. Our approach provides users with a series of operations in interactively constructing property graphs to describe their query intents. To efficiently explore massive RDF datasets, we convert RDF data into property graphs for storage. This can greatly reduce the size of massive RDF datasets and more importantly the converted property graphs can instruct users to understand the underlying structure of RDF datasets, which is very useful in users’ construction of their query property graphs. The constructed query property graphs are finally transformed into expressions with the query language of graph databases. With high-performance graph databases as query engines, we developed an RDF data exploration system – PG-Explorer. Through experiments over real-world datasets, we evaluated the effectiveness and superiority of our approach. The user study demonstrates that the proposed approach simplifies users’ exploration of RDF data and can satisfy their exploration needs.
Queue congestion prediction for large-scale high performance computing systems using a hidden Markov model

Abstract To share limited, large-capacity resources, the high-performance computing field provides services by allocating available resources to jobs through batch job schedulers. Therefore, it is natural that a queue waiting time occurs until the resources are available if resources are not sufficient. The prediction of queue waiting time is very useful to improve overall resource utilization. However, the queue waiting time is very difficult to predict because it is significantly affected by the many factors such as applied scheduling algorithm and characteristics of the executed job. In this study, a method of predicting queue waiting time using only the historical log data created by the batch job scheduler is examined. Specifically, a method of predicting queue waiting time based on a hidden Markov model is proposed. It has the following three stages. First, outliers are removed by applying the outlier detection algorithm using a statistics-based parametric method. Second, the parameters of the hidden state are estimated using the observed queue waiting time sequence based on the historical job log. Third, the queue waiting interval at time $$t+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>t</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> is provided using the estimated parameters at time t . Comparing the prediction accuracy with those of the other prediction methods, experimental results show that the proposed algorithm improves the prediction accuracy by up to 60%.
The use of the biological algorithm in solving applied mechanics design problems

Biologically inspired algorithms are becoming powerful in modern optimization. In this paper, the principles of a metaheuristic algorithm based on Harris hawks behavior are shown. The Harris Hawks Optimizer (HHO in short) was used for solving problems in applied mechanics (car side impact, cone clutch, three-dimensional beam and I beam optimization). In the end, a comparison of the results obtained by HHO and results obtained by other methods is given.
Summarizing Bengali Text: An Extractive Approach

Text summarization is a challenging task in the field of Natural Language Processing. In the case of lower resource language like Bengali is also a difficult task to make an automatic text summarization system. This paper is based on the extractive summarization of Bengali text. Text summarization deletes the less useful information in a piece of text or a paragraph and summarizes it into a confined text. This helps in finding the required text more effectively and quickly. There are many types of algorithms used for summarizing the text. Here in this paper, we have used TF-IDF and BERT-SUM technologies for the Bengali extractive text summarization.
Performing Presence with the Teaching-Body via Videoconferencing: A Postdigital Study of the Teacher’s Face and Voice

This paper addresses the dearth in research on embodied teaching by focusing on the body of the teacher, in particular their face, voice, use of silence, and their relationship with presence online. Postdigital positioning of the notion and experience of embodiment online posits a dynamic co-existence between technology and all aspects of human life including that which is social, physical, and material. Based on interviews conducted with five lecturers in different disciplines at a university in Singapore, and using the theoretical lens of Performance Studies, this paper presents an explanatory study that argues for the synergistic relationship between the teaching-body, presence, in/and online videoconferencing within the postdigital context. It argues that teaching presence takes place online and that the teaching-body is central to the performance of presence leading to a more meaningful educational experience.
Developing a novel steganography concept of audio data into audio streams

Steganography is the art of concealing a secret message in a non-secret object. Cyber criminals, on the other hand, employ steganography for more than just sending messages. Steganography is a data-hiding technique that secures data as it is sent from sender to recipient. The encoding technique of audio steganography is conducted out using a programme designed for embedding the audio info into a new audio type of streaming. It's all about secure and safe embedding data/info in a seemingly innocuous covered speech modules. Using the fundamentals of steganography, this system makes the files more secure. Secret communications are encoded in digital sound using a computer-based audio Steganography method. The hidden info is again put in back mode by changing the byte sequence of a audio file significantly. Texts in digital sounds are more difficult to embed in most circumstances than hidden messages in other media, such as digital photos. In order to successfully conceal covert conversations, a variety of ways for embedding information in digital audio have been created. Simple algos that hide informations in the form of signal to noise ratios to more complicated systems that use complex signal pattern recognition techniques are all examples of these strategies. The following sections delves deeper into these techniques.
Codes over Eisenstein integers for the Niederreiter cryptosystem

Large-scale quantum computers threaten today’s public-key cryptosystems. The code-based McEliece and Niederreiter cryptosystems are among the most promising candidates for post-quantum cryptography. Recently, a new class of q-ary product codes over Gaussian integers together with an efficient decoding algorithm were proposed for the McEliece cryptosystems. It was shown that these codes achieve a higher work factor for information-set decoding attacks than maximum distance separable (MDS) codes with comparable length and dimension. In this work, we adapt this q-ary product code construction to codes over Eisenstein integers. We propose a new syndrome decoding method which is applicable for Niederreiter cryptosystems. The code parameters and work factors for information-set decoding are comparable to codes over Gaussian integers. Hence, the new construction is not favorable for the McEliece system. Nevertheless, it is beneficial for the Niederreiter system, where it achieves larger message lengths. While the Niederreiter and McEliece systems have the same level of security, the Niederreiter system can be advantageous for some applications, e.g., it enables digital signatures. The proposed coding scheme is interesting for lightweight Niederreiter cryptosystems and embedded security due to the short code lengths and low decoding complexity.
An Improved Method of an Image Mosaic of a Tea Garden and Tea Tree Target Extraction

UAV may be limited by its flight height and camera resolution when aerial photography of a tea garden is carried out. The images of the tea garden contain trees and weeds whose vegetation information is similar to tea tree, which will affect tea tree extraction for further agricultural analysis. In order to obtain a high-definition large field-of-view tea garden image that contains tea tree targets, this paper (1) searches for the suture line based on the graph cut method in the image stitching technology; (2) improves the energy function to realize the image stitching of the tea garden; and (3) builds a feature vector to accurately extract tea tree vegetation information and remove unnecessary variables, such as trees and weeds. By comparing this with the manual extraction, the algorithm in this paper can effectively distinguish and eliminate most of the interference information. The IOU in a single mosaic image was more than 80% and the omissions account was 10%. The extraction results in accuracies that range from 84.91% to 93.82% at the different height levels (30 m, 60 m and 100 m height) of single images. Tea tree extraction accuracy rates in the mosaic images are 84.96% at a height of 30 m, and 79.94% at a height of 60 m.
Dissipation-Induced Information Scrambling in a Collision Model

In this paper, we present a collision model to stroboscopically simulate the dynamics of information in dissipative systems. In particular, an all-optical scheme is proposed to investigate the information scrambling of bosonic systems with Gaussian environmental states. Varying the states of environments, in the presence of dissipation, transient tripartite mutual information of system modes may show negative value, signaling the appearance of information scrambling. We also find that dynamical indivisibility based non-Markovianity plays dual roles in affecting the dynamics of information.
Research on the Hierarchical Teaching Method of University English Based on Artificial Intelligence Wireless Network

University English is one of the basic core courses for all university students. The course involves many difficult concepts and grammatical structures, so it is difficult to most students. The starting point in any teaching program is to determine whether teaching is needed to specify what that teaching should accomplish. So the students’ need is of vital importance to the success of university English teaching. Traditional university English teaching does not take into account students’ individual learning abilities and feedback, which would cause students to not grasp enough the key knowledge, and then make students lose their interest in English. The development of artificial intelligence technology is becoming more and more mature, especially in the application of English teaching, which promotes the reform, development, and modernization of English teaching. Hence, in this paper, we propose a hierarchical teaching method for the university English teaching platform and employ artificial intelligence to find the needs of university students and know about the mastering knowledge of students. Initially, the dataset is preprocessed using normalization, and then, the feature extraction is performed using principal component analysis (PCA). For classification of the data, we employ the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -means clustering algorithm. To enhance the evaluation system, the whale optimization algorithm (WOA) is used. The performance of the proposed system is analyzed, and it is shown that the average score of students who used the proposed platform to learn university English is far higher than those students who did not use the platform. Hence, the platform can improve students’ autonomous learning and English abilities.
Exploiting Inactive Examples for Natural Language Generation With Data Rejuvenation

Recent years have witnessed the success of natural language generation (NLG) accomplished by deep neural networks, which require a large amount of training data for optimization. With the constant increase of data scale, the complex patterns and potential noises make training NLG models difficult. In order to fully utilize large-scale training data, we explore inactive examples in the training data and propose to rejuvenate the inactive examples for improving the performance of NLG models. Specifically, we define inactive examples as those sentence pairs that contribute less to the performance of NLG models, and show that their existence is independent of model variants but mainly determined by the data distribution. We further introduce <i>data rejuvenation</i> to improve the training of NLG models by re-labeling the inactive examples. The rejuvenated examples and active examples are combined to train a final NLG model. We evaluate our approach by experiments on machine translation (MT) and text summarization (TS) tasks, and achieve significant improvements of performance. Extensive analyses reveal that inactive examples are more difficult to learn than active ones and rejuvenation can reduce the learning difficulty, which stabilizes and accelerates the training process of NLG models and results in models with better generalization capability.
Developing a Chatbot system using Deep Learning based for Universities consultancy

Inspired by the recent successes of Deep Learning on Natural Language Processing (NLP), we propose a chatbot system using Deep Learning for Vietnamese Universities consultancy which can be implemented for any university. The system has three important tasks: User&#x2019;s Intent Recognition, Dialogue Management, and Reply Channels. For the User&#x2019;s Intent Recognition task, the Pattern Matching method is combined with the Text Classification model using Bidirectional-LSTM which has the Attention mechanism. Besides, we use the Deep Reinforcement Learning architecture to train an Agent for Dialogue Management task. In this paper, we conduct a Proof of Concept to the Ho Chi Minh City University of Technology (HC-MUT). The experimental results achieve an average 89% F1-score of 3 classes of the Text Classification task using Deep Learning, the evaluation result of Dialogue Management that the rate of success achieves by 86%. Our demo version of a production web application is available at https://hcmutbot.herokuapp.com/.
Self-born Wiring for Neural Trees

Neural trees aim at integrating deep neural networks and decision trees so as to bring the best of the two worlds, including representation learning from the former and faster inference from the latter. In this paper, we introduce a novel approach, termed as Self-born Wiring (SeBoW), to learn neural trees from a mother deep neural network. In contrast to prior neural-tree approaches that either adopt a pre-defined structure or grow hierarchical layers in a progressive manner, task-adaptive neural trees in SeBoW evolve from a deep neural network through a construction-by-destruction process, enabling a global-level parameter optimization that further yields favorable results. Specifically, given a designated network configuration like VGG, SeBoW disconnects all the layers and derives isolated filter groups, based on which a global-level wiring process is conducted to attach a subset of filter groups, eventually bearing a lightweight neural tree. Extensive experiments demonstrate that, with a lower computational cost, SeBoW outperforms all prior neural trees by a significant margin and even achieves results on par with predominant non-tree networks like ResNets. Moreover, SeBoW proves its scalability to large-scale datasets like ImageNet, which has been barely explored by prior tree networks.
The Use of Mobile Learning in Higher Education: A Study on the MOOC of Cadi Ayyad University

With the rapid growth of mobile technologies, the evolution of mobile devices, and the progress in wireless communications and networking technologies, mobile devices have become very popular and have reached a very high spread. Consequently, mobile learning (m-learning) has become a prominent phenomenon and has revolutionized the entire education system. This paper aims to highlight the significant importance of mobile devices in the field of e-learning in higher education. To do so, we have conducted a study on a massive open online course (MOOC) created and implemented specifically to respond and blend into overcrowded classrooms and Amphitheaters. The obtained results showed that the integration and the use of m-learning facilitated the learners’ access to the teaching resources and materials offered by the University, and its promoted academic learning in classrooms and amphitheaters, in order to enhance the learning outcomes of the learners.
Mask wearing Detection Based on Improved YOLOv3

Mask-wearing detection in public places is a very important and challenging task under the situation of normalized epidemic prevention and control. There is high error rate of mask- wearing detection by use of traditional YOLOv3 obviously. In this paper, an improved YOLOv3 object detection method was proposed. In order to improve the method’s insufficient ability to detect small objects, the following improvements were made in the paper. The number of anchor boxes was increased and the activation function was replaced to improve the accuracy. The Focal Loss was used to improve the model convergence speed. In this paper, a dataset with a total of 2707 images was built by taking pictures from smartphones and acquiring relevant pictures on the Internet. The results of the comparative experiment show that the mAP (mean average precision) of the proposed is improved 1.33% than others.
The Desirable Systemic Uncertainty in Complex IoT Sensor Networks—General Anticipatory Foresight Perspective

A wide methodological spectrum regarding future research is offered by anticipation studies, with a special role of foresight studies. Many studies of this type focus on generating the desired future, taking into account the fact that it is accompanied by uncertainty. The author of this publication postulates treating uncertainty as an equivalent-in relation to the future-research object. This approach allows us to formulate general assumptions for a model of the anticipatory management of systemic uncertainty in IoT networks. The goal of such a model will not be to eliminate or even minimize uncertainty, but to regulate it to a desired level. Such an action can bring many more benefits than trying to zero out uncertainty. On the general side, uncertainty can be studied in two ways: (1) as an abstract idea, or (2) as a feature of a particular structure, also with elements of research on its abstract component. In this paper the attention is focused on the second approach. The main research area is the IoT network in its broadest sense, with a particular role of the social construct, in the context of the study of systemic uncertainty in relation to selected anticipatory actions. The overarching goal is to define a desired state, or to determine what such a desired state is, of anticipatory IoT uncertainty.