Reviewerly/exHarmony huggingface dataset:
A Lattice-Based Authentication Scheme for Roaming Service in Ubiquitous Networks with Anonymity

In the ubiquitous networks, mobile nodes can obtain roaming service that enables them to get access to the services extended by their home networks in the field of foreign network. To provide secure and anonymous communication for legal mobile users in roaming services, there should be a mutual authentication between mobile user and foreign agent with the help of home agent. There are many roaming authentication schemes which have been proposed; however, with the progress of quantum computation, quantum attack poses security threats to many traditional public key cryptography-based authentication schemes; thus, antiquantum attack roaming authentication schemes need to be investigated. On account of the limitation of computational resources for mobile nodes, a lightweight anonymous and antiquantum authentication schemes need to be developed to enable mobile nodes to roam across multiple service domains securely and seamlessly. In consideration of the advantages of lattice in antiquantum, an NTRU-based authentication scheme with provable security and conditional privacy preservation is proposed to remedy these security weaknesses. Compared with the existing scheme, the proposed scheme not only improves efficiency but also can resist the quantum attack.
A Neighbor-Guided Memory-Based Neural Network for Session-Aware Recommendation

Session-aware recommendation is a special form of session-based recommendation, where users' historical interactions before the current session are available.Among the existing session-aware recommendation studies, recurrent neural network (RNN) is a popular choice to model users' current intent of the ongoing session as well as their general preference implied in previous sessions.However, these RNN-based methods present limited memory so as to have difficulty in characterizing long-term behaviour.In addition, when modeling a user's preference, existing studies mainly refer to his (her) own sessions, while ignore the collaborative information from sessions of other users that may share similar tastes.To tackle the above problems, we propose a neighbor-guided memory-based neural network (MNN) for session-aware recommendation task, which comprehensively considers users' short-term intent, long-term preference and cross-sessions information to yield the final recommendations.Specifically, we design a long-term memory generator to capture users' general preference from their historical sessions, meanwhile leverage the neighbor sessions of current session to obtain cross-session collaborative information.Furthermore, with the guidance of long-term memory and cross-session information, we employ a short-term memory generator to yield users' ongoing intent, which serves as the dominating part of recommendation.Extensive experimental results on three real world datasets show the effectiveness of our model, with improvements up to 12.0% on 30MUSIC, 26.5% on NOWPLAYING and 13.5% on TMALL in terms of Recall@20, respectively.Analysis on current session length and the number of historical interacted items comprehensively demonstrates the superiority of our proposal on processing long sessions and modeling users' long-term behaviour.
PSYCHOLOGICAL ASPECTS OF DEVELOPINGCREATIVE PERSONALITY AND THE CONCEPT OFREDUCTION OF CREATIVITY TO INTELLECT

This article is devoted to the socio-psychological factors of formation of independent thinking,creativity in students, analyzes the peculiarities of the formation of creative thinking in students throughinteractive teaching methods that stimulate independent thinking, independent behavior and behavioral,cognitive approaches to learning. The article also covers the process of educating creative person and itspsychological mechanisms, components, the concept of reduction of creativity to the intellect, the role of newgeneration textbooks in the education of a creative person.
Digital Core 3D Reconstruction Based on Micro-CT images via a Deep Learning Method

Digital core technology is the cutting-edge technology of petroleum engineering. Digital core 3D reconstruction is a key issue in digital core technology. At present, the most accurate 3D reconstruction method of digital core is X-Ray Computed Tomography. However, this method is too expensive to afford even for the big oil companies. Especially with the current low oil prices, oil companies are looking to reduce costs. There are two reasons for the high cost of X-ray computed tomography equipment: first, as the accuracy of the equipment improves, the cost of the sensor and the supporting circuit board rises linearly; second, the traditional method of establishing a digital core requires scan the core layer by layer continuously, which allows the sensor to process only a limited number of cores throughout the entire life cycle, increasing the cost of rebuilding a single core. In response to this problem, we propose a new method to make the sensor no longer scan rock core layer by layer. The workflow is as follows: the unscanned layers are interpolated through the values of scanned layers, and then use deep learning algorithm to improve the interpolation quality. The feasibility experiments show that the to-be-established digital core can be obtained with 10 scanned layers instead of 73 scanned layers. This is actually equivalent to using more calculations to release the pressure on the x-ray sensor and its circuit board. Although this method requires the device to do additional calculations, it greatly reduces the workload of the x-ray sensor and its supporting circuits. Since computing equipment such as CPU and GPU are much cheaper than them, our proposed method has practical value.
Prospects, Problems and Ways to Improve Distance Learning of Students of Higher Educational Institutions

Today, the structure of education in the leading countries of the world is changing significantly.It is innovation that comes to the fore, the introduction of which can contribute to effective educational development.Separately, the relevance of the study of distance learning is added by the fact that the world lives in a pandemic, which requires a review of the current system of higher education and transfer it to the distance.The main purpose of the article is to study the problem and ways of improving the distance learning of students of higher education.Distance learning provides the opportunity to study when it is convenient for the student, at the pace that he chooses (within the established terms of the courses), in the place where he is (does not need to spend time on the road to universities to take current, and sometimes and final controls).The methodological basis is a system of complementary general scientific and special methods of cognition of socio-political phenomena, in particular systemicstructural, comparatively retrospective and structural-functional, interviewed, general methods of analysis and synthesis, etc.As a result, the problems and prospects of improving distance learning of scientific disciplines were investigated and the essence of the problems in the teaching of disciplines and ways to solve them were revealed.It is recommended that use proposed model of distance learning.
Biology in the grid: Graphic design and the envisioning of life

"Biology in the grid: Graphic design and the envisioning of life." Visual Studies, ahead-of-print(ahead-of-print), pp. 1–2
Quantum CNOT Circuits Synthesis for NISQ Architectures Using the Syndrome Decoding Problem

Current proposals for quantum compilers involve the synthesis and optimization of linear reversible circuits and among them CNOT circuits. This class of circuits represents a significant part of the cost of running an entire quantum circuit and therefore we aim at reducing the size of CNOT circuits. In this paper we present a new algorithm for the synthesis of CNOT circuits based on the solution of the syndrome decoding problem. Our method addresses the case of ideal hardware with an all-to-all qubit connectivity and the case of near-term quantum devices with restricted connectivity. Benchmarks show that our algorithm outperforms existing algorithms in both cases of partial and full connectivity.
Polybius Square in Cryptography: A Brief Review of Literature

Cryptography is a method to secure sensitive data for storage and communication in the presence of third parties called adversaries.One of the first recorded incidents of cryptography occurred in Ancient Rome, where the Polybius cipher, also known as Polybius square, was developed.The Polybius cipher has been used as an advantage in winning wars over enemies according to history and is also utilized as a medium of communication.Today, as there is a need for more secure communication, the Polybius cipher, despite its early discovery and usage, is still at par when it comes to serving its purpose; to encrypt a secret message to provide secure information.There is a need for a secure cryptographic performance, and Polybius cipher is being improved and is hybridized along with other algorithms in the quest to achieve better system performance.This paper shows a brief literature review on the use of Polybius square in cryptography.
Analysis NaÃ¯ve Bayes In Classifying Fruit by Utilizing Hog Feature Extraction

Indonesia has abundant natural resources, especially the results of its plantations. Lots of local fruit that can be used starting from the root to the skin of the fruit. Local fruit can be consumed as fresh fruit and can also be processed into drinks and food. This is reflected in the diversity of tropical fruits found in Indonesia. Fruits that are rich in benefits and can be used as medicines such as Apples, Avocados, Apricots, and Bananas. These fruits are often found around us. In Indonesia these fruits are produced and also exported abroad. However, the limited methods and technology used to classify this fruit are interesting things to discuss and become the main focus in this research. This study analyzed using the NaÃ¯ve Bayes algorithm and feature extraction of HOG (Oriented Gradient Histogram) to obtain more effective classification results. The results showed that the collection of fruit using the NaÃ¯ve Bayes method and HOG feature extraction had not yet obtained maximum classification results, only with an accuracy of 56.52%.Keywords â€“ Apple, Avocado, Apricot, Banana, NaÃ¯ve Bayes, HOG.
K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining

Coronary heart disease is a disease that often causes human death, occurs when there is atherosclerosis blocking blood flow to the heart muscle in the coronary arteries. The doctor's referral method for diagnosing coronary heart disease is coronary angiography, but it is invasive, high risk and expensive. The purpose of this study is to analyze the effect of implementing the k-Fold Cross Validation (CV) dataset on the rule-based feature selection to diagnose coronary heart disease, using the Cleveland heart disease dataset. The research conducted a feature selection using a medical expert-based (MFS) and computer-based method, namely the Variable Precision Rough Set (VPRS), which is the development of the Rough Set theory. Evaluation of classification performance using the k-Fold method of 10-Fold, 5-Fold and 3-Fold. The results of the study are the number of attributes of the feature selection results are different in each Fold, both for the VPRS and MFS methods, for accuracy values obtained from the average accuracy resulting from 10-Fold, 5-Fold and 3-Fold. The result was the highest accuracy value in the VPRS method 76.34% with k = 5, while the MTF accuracy was 71.281% with k = 3. So, the k-fold implementation for this case is less effective, because the division of data is still structured, according to the order of records that apply in each fold, while the amount of testing data is too small and too structured. This affects the results of the accuracy because the testing rules are not thoroughly represented
An optimization‐based decision support system for students' personalized long‐term course planning

Abstract This paper intends to facilitate academic advising by means of optimal long‐term course planning (LTCP), which is instrumental in students' retention and success. LTCP ensures that students will satisfy the educational requirements without facing unnecessary delays. The proposed decision support system is based on an optimization model which deals with several student preferences, that is, maximum credits per semester, difficulty of courses, graduation semester, and elective courses priorities. The proposed system has been implemented in an industrial engineering department, and several improvements and modifications have been done based on student feedback. The developed system supports two kind of users, that is, educational staff and the students. The courses, their relationships, and regulation parameters are defined/set by educational staff, whereas students determine their own information and preferences, for example, finished courses, graduation target, interest for each elective course, complexity of courses, course load and complexity for each semester, and relative weights of objective functions. Considering theoretical issues, common applications have been integrated to form this system with simple and user‐friendly interfaces.
Human-Object Interaction: Application to Abandoned Luggage Detection in Video Surveillance Scenarios

CCTV systems bring numerous advantages to security systems, but they require notable efforts from human operators in case of alarming events in order to detect the precise triggering moments. This paper proposes a system that can automatically trigger alarms when it detects abandoned luggage, detects the person that left the baggage and then tracks the suspicious person throughout the perimeter covered by a CCTV system. The system is based on Mask R-CNN and has been tested with several backbone configurations. Wee valuate each subsystem independently on datasets specific for their task. The network model proves to be robust enough to carry on all of the three different tasks as demonstrated by tests.
BlocklyAR: A Visual Programming Interface for Creating Augmented Reality Experiences

State-of-the-art tools for creating augmented reality (AR) applications often depend on a specific programming language and the deployed target devices. The typing syntax of a program is error-prone, and device dependency makes it difficult to share newly created AR applications. This paper presents BlocklyAR, a novel web-based visual programming interface for creating and generating an AR application. This tool is intended for non-programmers (young learners and enthusiasts) who are interested in making an AR application. The goals of this tool are: (1) to help young learners and enthusiasts express their programming ideas without memorizing syntax, (2) to enable users to perceive their expressions, (3) to enable learners to generate an AR application with minimal effort, and (4) to support users by allowing them to share newly created AR applications with others. BlocklyAR uses Blockly for creating a palette of commands and AR.js for transcribing commands into AR experience. The applicability of BlocklyAR was demonstrated through a use case where an existing AR application was recreated by using our tool. The result showed that our tool could yield an equivalent product. We evaluated the visual tool with the help of 66 users to gather perspectives on the specific benefits of employing BlocklyAR in producing an AR application. The technology acceptance model was adapted to assess an individual’s acceptance of information technology.
BLiMP: A Benchmark of Linguistic Minimal Pairs for English

We introduce The Benchmark of Linguistic Minimal Pairs (shortened to BLiMP), a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars, and aggregate human agreement with the labels is 96.4%. We use it to evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs. We find that state-of-the-art models identify morphological contrasts reliably, but they struggle with semantic restrictions on the distribution of quantifiers and negative polarity items and subtle syntactic phenomena such as extraction islands.
An Efficient Approach for Detecting and Classifying Moving Vehicles in a Video Based Monitoring System

Moving objects detection, type recognition, and traffic analysis in video-based surveillance systems is an active area of research which has many applications in road traffic monitoring. This paper is on using classical approaches of image processing to develop an efficient algorithm for computer vision based on traffic surveillance system that can detect and classify moving vehicles, besides serving some other traffic analysis issues like finding vehicles speed and heading, tracking specified vehicles, and finding traffic load. The algorithm is designed to be flexible for modification to fulfill the changes in design objectives, having limited computation time, giving good accuracy, and serves inexpensive implementation. A 92% of success is achieved for the considered test, with the missed cases being abnormal that are not defined to the algorithm. The computation time, with a platform (hardware and software) dependent, the algorithm took to produce results was parts of milliseconds. A CNN based deep learning classifier was built and evaluated to judge the feasibility of involving a modern approach in the design for the targeted aims in this work. The modern NN based deep learning approach is very powerful and represents the choice for many very sophisticated applications, but when the purpose is restricted to limited requirements, as it is believed the case is here, the reason will be to use the classical image processing procedures. In making choice, it is important to consider, among many things, accuracy, computation time, and simplicity of design, development, and implementation.
Direct Short-Term Forecast of Photovoltaic Power through a Comparative Study between COMS and Himawari-8 Meteorological Satellite Images in a Deep Neural Network

Meteorological satellite images provide crucial information on solar irradiation and weather conditions at spatial and temporal resolutions which are ideal for short-term photovoltaic (PV) power forecasts. Following the introduction of next-generation meteorological satellites, investigating their application on PV forecasts has become imminent. In this study, Communications, Oceans, and Meteorological Satellite (COMS) and Himawari-8 (H8) satellite images were inputted in a deep neural network (DNN) model for 2 hour (h)- and 1 h-ahead PV forecasts. A one-year PV power dataset acquired from two solar power test sites in Korea was used to directly forecast PV power. H8 was used as a proxy for GEO-KOMPSAT-2A (GK2A), the next-generation satellite after COMS, considering their similar resolutions, overlapping geographic coverage, and data availability. In addition, two different data sampling setups were designed to implement the input dataset. The first setup sampled chronologically ordered data using a relatively more inclusive time frame (6 a.m. to 8 p.m. in local time) to create a two-month test dataset, whereas the second setup randomly sampled 25% of data from each month from the one-year input dataset. Regardless of the setup, the DNN model generated superior forecast performance, as indicated by the lowest normalized mean absolute error (NMAE) and normalized root mean squared error (NRMSE) results in comparison to that of the support vector machine (SVM) and artificial neural network (ANN) models. The first setup results revealed that the visible (VIS) band yielded lower NMAE and NRMSE values, while COMS was found to be more influential for 1 h-ahead forecasts. For the second setup, however, the difference in NMAE results between COMS and H8 was not significant enough to distinguish a clear edge in performance. Nevertheless, this marginal difference and similarity of the results suggest that both satellite datasets can be used effectively for direct short-term PV forecasts. Ultimately, the comparative study between satellite datasets as well as spectral bands, time frames, forecast horizons, and forecast models confirms the superiority of the DNN and offers insights on the potential of transitioning to applying GK2A for future PV forecasts.
A Novel Architecture for Internet of Things Based E-Health Systems

The emerging Internet of Things (IoT) technology has revolutionized existing medical devices to act smartly towards creating digital world related to health. IoT endeavors an enormous promise in the area of electronic health (e-health) where existing technologies have been deployed to provide rapid access to patient care. IoT based e-health systems involve significant technologies and connected things to monitor, identify, track, manage and store patients' information for ongoing healthcare. A mechanism is required that assures interoperability between devices for processing and use of resources in an efficient manner for the successful deployment of IoT within e-health. The study has coined a unique idea through an exclusive induction of e-governance layer in the concept and implementation of IoT based e-health systems. Therefore, the study targets to develop a novel architecture for incorporation of IoT in the e-health systems by intensively focusing its different layers i.e., sensing, application, transmission, fog and e-governance for having emphasis to get quick response and secured transmission with improved healthcare services. An exploratory study has been conducted to investigate existing literature regarding cutting-edge developments in the arena of e-health. The proposed IoT based e-health architecture is extremely impressive novelty with much comprehensiveness of the concept with the inclusion of very a crucial but previously unnoticed component of Emergency Medical Services (EMS) having its integration through blockchain technology. Another novelty has been presented with the idea of explicitly defining the e-governance layer that encompasses all layers of the proposed architecture in order to achieve quality healthcare services.
Automated optimization for memory‐efficient high‐performance deep neural network accelerators

The increasing size and complexity of deep neural networks (DNNs) necessitate the development of efficient high-performance accelerators. An efficient memory structure and operating scheme provide an intuitive solution for high-performance accelerators along with dataflow control. Furthermore, the processing of various neural networks (NNs) requires a flexible memory architecture, programmable control scheme, and automated optimizations. We first propose an efficient architecture with flexibility while operating at a high frequency despite the large memory and PE-array sizes. We then improve the efficiency and usability of our architecture by automating the optimization algorithm. The experimental results show that the architecture increases the data reuse; a diagonal write path improves the performance by 1.44× on average across a wide range of NNs. The automated optimizations significantly enhance the performance from 3.8× to 14.79× and further provide usability. Therefore, automating the optimization as well as designing an efficient architecture is critical to realizing high-performance DNN accelerators.