Reviewerly/exHarmony huggingface dataset:
Manto: A Practical and Secure Inference Service of Convolutional Neural Networks for IoT

As convolutional neural networks (CNNs) exhibit remarkable performance in various inference tasks, it is increasingly important to enable Internet of Things (IoT) devices to perform CNN-based applications. Many companies provide their carefully trained neural networks as inference services for resource-constrained clients (e.g., IoT devices). However, the use of CNN inference in many IoT applications raises privacy concerns. Cryptographic inference services provide a way to perform neural inference efficiently and, at the same time, preserve both the privacy of the client’s input data and the server’s proprietary model. Unfortunately, the existing solutions incur severe latency costs, stemming mostly from nonlinear activations such as ReLUs, which make them still unsuitable for deployment in real IoT devices. In this article, we propose Manto, a secure inference system of CNNs for IoT. Manto makes the following two specific efforts by combining the insights of machine learning and cryptography. First, we customize different quadratic activation functions to replace specific ReLU layers and further propose a sliding-window-based fine-tuning method to produce CNN models involving no or few ReLUs. These techniques allow us to speedup cryptographic inference and guarantee inference accuracy. Second, we develop a series of cryptographic protocols that support ReLU activations and its approximation variants (i.e., polynomial activations), which purely rely on the lightweight secret sharing techniques in the online execution and can well cope with the above-mentioned optimized CNN models in the ciphertext domain. Our experimental results show Manto obtains state-of-the-art performance, reducing online inference latency by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$66.2\%\sim 87.7\%$ </tex-math></inline-formula> over prior works on CIFAR-100 and TinyImageNet data sets.
Opposition-based learning boosted orca predation algorithm with dimension learning: a case study of multi-degree reduction for NURBS curves

Abstract The approximate degree reduction of ball non-uniform rational B-splines (NURBS) curves is a pivotal and knotty technique in computer-aided design/computer-aided manufacture. As we all know, the multi-degree reduction of NURBS ones is a mathematical optimization problem that a swarm intelligence algorithm can deal with. This paper uses an improved orca predation algorithm (IOPA) to accomplish the optimal multi-degree reduction of NURBS curves. Firstly, by incorporating a dimension learning strategy and opposition-based learning strategy into the orca predation algorithm (OPA), an IOPA is developed to increase the population diversity and enhance its capability of jumping out of the local minima. Secondly, the superiority of the proposed IOPA is comprehensively verified by comparing it with the original OPA and numerous celebrated and newly developed algorithms on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite and IEEE CEC2017 benchmark functions, respectively. Meanwhile, the practicability of IOPA is also highlighted by solving three real-world engineering design problems. Furthermore, statistical testing of IOPA has been conducted to validate its significance. Finally, the optimization model of multi-degree reduction for NURBS curves is established by minimizing the distance between the original curve and the approximate curve. The IOPA is utilized to solve the optimization model, and the optimal approximate NURBS curves are obtained. Some representative numerical examples illustrate the ability of the proposed IOPA to effectively solve the multi-degree reduction problem of NURBS curves in terms of precision, robustness, and convergence characteristics.
Few-shot link prediction with meta-learning for temporal knowledge graphs

Abstract With the deepening of the research on knowledge graph embedding, temporal knowledge graphs (TKGs), which are dynamic changes over time, have gradually gained the attention of researchers. Although some TKG-embedding models have been proposed, they did not perform well for certain relationships with insufficient samples, since they all require tremendous training samples. Thus, few-shot link prediction tasks, namely predicting new relation-specific quadruples by observing only a few samples, are still very challenging. In this paper, a method named meta-reasoning for TKGs (MetaRT) is proposed to solve this universal but challenging problem. MetaRT works by extracting the meta-information of a specific relation and updating it quickly, so that the model can learn the most critical information in TKG swiftly and independently. In the meantime, temporal information can be managed well by a TKG learner. Finally, through a large number of experiments, it shows that MetaRT outperforms other existing TKG-embedding models on the problem of few-shot learning.
Material and textural features-based retrieval using feature similarity mapping for synthetic images

Content Based image retrieval (CBIR) is one of the prominent application of image processing where image retrieval from the database is made automatic based on the content in it. The materialist details of the images contains high degree of information and can serve as important toll for analysis. Different variant of content based image retrieval work by locating an object in the image and then retrieving images with similar object in it. The method works using different similarity assessment techniques. In this research work, we have developed a new material feature and HMMD space CBIR technique which works by transforming the domain to HMMD space and then finding the material and Texture Features along with other relevant features. These features forms he feature vector and are then deployed to assess the similarity of the two images which then generates a relevant and irrelevant image search. The system has higher accuracy of assessment as verified by experimentation for precision, productivity and precision agriculture.
ST – ELEMENTS APPLICATIONS FOR SOME TASKS

Using elements of the mathematics of St1, we introduce the concept of St – the change in physical quantity B: . Then the mean St - velocity will be vcpst(t, Δt) =, and St is the velocity at time t:. St – acceleration .
ENHANCING COLLEGE CASIO BOT ASSISTANT WITH COMPUTER INTERFACE AND SPEECH RECOGNITION

College Casio Bot is a virtual assistant, it understands and responds to the query asked by users in their own language.College Casio bot provides two modes like text mode and voice mode for better user experience.It displays the 3D view and tour of the institution.It provides the information about the departments, Hod's of the respective department, faculties and staffrooms with images and videos, the student's admission details, important circulars and latest updates regarding the university or department.The Casio bot is available 24/7 to answer the queries, it helps the students and parents to clarify their doubts through simple English language and it assist the visitors to navigate to respective department.It saves time and provides High speed and accurate response, reduces paper work.Students and parents will utilize the bot instead of making queue at enquiry desk to ask the queries.
Image processing using the quantum quaternion Fourier transform

There is a growing interest in quantum image processing (QIP) that rose from the desire to exploit the properties of quantum computing to improve the performance of classical techniques and their applications. Since the introduction of a quaternion by Hamilton in 1843, quaternions had been used in a lot of applications. We show how the quantum states and quantum operators work in the field of quaternions and how we can use it to extend the quantum complex Fourier transform (QCFT) to the quantum quaternion Fourier transform (QQFT). We propose for the first time the novel algorithm for the QQFT. We explain the impossibility of the use of convolution in quantum image processing; we design filters in the quaternion Fourier spectrum and the space domain an edge detector and a quantum median filter.
Minimum distance and parameter ranges of locally recoverable codes with availability from fiber products of curves

We construct families of locally recoverable codes with availability $$t\ge 2$$ using fiber products of curves, determine the exact minimum distance of many families, and prove a general theorem for minimum distance of such codes. The paper concludes with an exploration of parameters of codes from these families and the fiber product construction more generally. We show that fiber product codes can achieve arbitrarily large rate and arbitrarily small relative defect, and compare to known bounds and important constructions from the literature.
USE OF HIGHLIGHTING STRATEGIES IN READING COMPREHENSION AND EFFECTS ON ATTAINMENT IN SELECTED READING SKILLS

This study had two specific research objectives, two research questions and four specific research hypotheses. The study used quasi-experimental research designs. Data were collected using a standardized test. The study had a population of English Language students from secondary schools. The purposive sampling technique was used. The sample consisted of 40 students. The reliability of the instruments was established using Cronbach Alpha, which yielded a reliability coefficient of .70. Data collected were analyzed using the Statistical Package for Social Sciences (SPSS version 24.00). Findings revealed that reading comprehension performances of students taught skimming and scanning in the highlighting strategies were significantly higher than those who were not taught using the highlighting strategies.
Customer Satisfaction on Quality of ISO Standard 9126 Services in Electronic Banking in Libya

Despite the availability of some electronic banking (e-banking) services in Libya, these services are still facing difficulties in many ways, they have advantages and disadvantages, and so far, they have not been understood by some customers to get their satisfaction. This research aims to know the extent of customer satisfaction (CS) with e-banking services in Libya through ISO 9126 standards. The research population consisted of all customers of (Al-Wahda) and (Commerce &amp; Development (C&amp;D)) bank in Benghazi city, a random sample of 180 and 207 clients were selected respectively. Research hypotheses have been tested, and we noticed a relationship between the existence of a strong significant correlation between the quality services of e-banking according to quality standards ISO 9126 software and CS among the banks under study. The value of this research comes from the new scientific results to study the impact of the quality of e-banking services on CS in Libyan banks, and its originality lies because of the lack of this research applied to Libyan banks in general and Benghazi city in particular.
Study of Influential Mechanism of Workplace Loneliness and Knowledge Hiding

In the digital era, enterprises need a series of innovative practices to stand out in the fierce market competition. The role of unique intellectual resources is particularly important in maintaining competitive advantages compared to other replicable production resources. Knowledge is an intangible asset that requires a people carrier in order to be used and circulated in organizations, innovation is often born in this process. Managers have invested a lot of resources to facilitate knowledge sharing, but to protect their personal interests, employees may implement knowledge hiding to maintain their own assets and unique status, so that making knowledge management practices have little effect in organizations. Knowledge hiding behavior has gradually become a topic of academic concern in the field of knowledge management. In this era, the epidemic has further driven changes in employees' work environment and methods, reducing human contact and giving rise to workplace loneliness. In recent years, domestic scholars have also begun to pay attention to this negative emotional experience in the workplace and have conducted studies on negative work behaviors triggered by workplace loneliness. In this study, we focus on the mechanism of workplace loneliness on knowledge hiding behavior and construct a research model based on the resource conservation theory and the job demands - resources model. Specifically, workplace loneliness is used as the independent variable to influence knowledge hiding through the mediating effect of emotional exhaustion, and high involvement work system is introduced as the boundary condition to verify its moderating effect on the first and second stages of the model.
Deteksi Penyakit pada Tanaman Padi Menggunakan Pengolahan Citra Digital dengan Metode Convolutional Neural Network

Rice plant is one of the important factors in supporting human life. When it starts to grow, of course, rice plants also often face problems such as pests or diseases that cause plants to die and lead to crop failure. So proper handling is needed to overcome the disease in rice plants. One of the treatments that can be done is by detecting diseases in rice plants, so that farmers can provide appropriate treatment for these problems. The research data will be processed through several stages, then the dataset used in this study consists of three classes of rice plant diseases, namely, bacterial leaf blight, brown spot, leaf smut and one class of healthy/healthy rice plants with a total of 16000 datasets collected from sources www.kaggle.net and previous research. The parameters tested in this study, namely hidden layer and optimizer affect system performance in the form of accuracy, precision, recall, fl-score, and loss values. In this study, the best results were obtained by using four hidden layers and Adam optimizer. Accuracy was 99.66%, precision, recall, fl-score was 99.66%. 100% and a loss of 0.0047 as well as a graph of the accuracy and loss performance in a good fit.
Application of the Game Based Learning Model to Improve Students' Concept Understanding Ability at SMP Negeri 35 Medan

The purpose of this study was to determine the effect of applying a game-based learning model that can improve students' ability to understand mathematical concepts in the matter of number patterns at SMP Negeri 35 Medan. The type of research is classroom action research with 32 students in class VIII-5 SMP Negeri 35 Medan as subjects. From the data analysis, in the first cycle, 18 students (64.28%) achieved completeness, while 14 students (35.72%) did not complete it with an average grade of 73.66. In cycle II, students who had achieved mastery increased to 24 students (85.71%) while 8 students (14.29%) had not completed with an average grade of 86.38. So it can be concluded that the ability to understand mathematical concepts of SMP Negeri 35 Medan students increased by applying the game-based learning model.
Deteksi Kelas Ruangan Berdasarkan Reverberation Time dengan Metode Linear Predictive Coding (LPC) dan K-Nearest Neighbor (KNN)

Identification of the size of the evidence file conversation can be one of the tools for various purposes such as in the police world. Determining the class of the room from the recording can be an additional clue in case processing. One way for the police to identify the class of a room is by creating a room class detection system. The class of the room can be determined by measuring the reverberation time using the LPC algorithm by extracting the characteristics of the training data in the form of audio. After obtaining the characteristics, the system will store these characteristics in the form of a dataset for testing. Then, the test data for which the room class is not yet known is inputted into the test system. KNN will classify the test data based on the previously trained dataset. The last process of the system will issue the value of accuracy and computational time from system testing. This study uses MATLAB calculation software as a calculation and simulation process, using 63 training data and 18 test data. The accuracy of the system test for detecting room class based on reverberation time using the LPC and KNN methods has resulted in a number with the largest accuracy value of 83.33% and computation time along 4,94657 seconds with a K value of 3, LPC order of 12, number of frames 240, and the Hanning window.
STATUS OF ELT AT THE SECONDARY LEVEL IN KARBI ANGLONG DISTRICT: A DISCUSSION

English language teaching (ELT) should be effective for all concerned at the secondary level to meet its objectives so that in higher levels the learners as well as teachers would carry out the process without constraints. Keeping view of the above, a study is conducted amongst the secondary institutions of Karbi Anglong District of Assam. In this regard, 50 institutions are surveyed to find out the status of ELT through field investigation, interviews with both learners and teachers. The survey found out some observations that reveal a very poor status of ELT at secondary level that consequently affects the teaching of English at the senior secondary and undergraduate levels. This article discusses the status of ELT at the secondary level and the reasons behind its weakness in the said region. It analyses how the teachers of undergraduate institutions face trouble to deal with the students coming out of those secondary schools. It also tries to find out the reasons, and puts forward suggestions and recommendations to develop the present status of ELT.
Near and Far Interaction for Outdoor Augmented Reality Tree Visualization and Recommendations on Designing Augmented Reality for Use in Nature

Abstract The application of augmented reality in nature and in the context of environmental education is not common. Moreover, augmented reality is often used mainly for visualization, which puts the user in a rather passive state. To promote a more active user experience, we have created an interactive AR environment for outdoor scenarios. In this article, we investigate how near and far interaction using a head-mounted display can be combined with visualization on a tree. We compare both interaction techniques to decide which is more suitable for future use. We present some educational use cases and investigate the interaction with virtual leaves on the ground in combination with a virtual-real interaction with a physical tree. Parameters such as type of interaction, different real environments, and task performance time as well as the combination and interconnection between them are discussed and studied. In addition, process visualizations in nature, such as clouds and rain, and tree root growth are included in the augmented reality modules and are evaluated in user tests followed by questionnaires. The results show that both near and far interaction can be beneficial for a future educational application. We further present a number of outdoor-specific recommendations for AR design and usage to support future researchers and AR practitioners outdoors in the nature.
Peculiarities of personality socialization in current conditions

Socialization is determined by nature of society, its characteristics and needs. Criteria and standards of living, conditions of individual’s creative self-realization are transformed together with changes occurring in the society. In this regards, of peculiar relevance are investigations of changes characterizing a process of socialization; determining of certain reasons, circumstances and social differences which specify moulding a personality as a society member.
Understanding the domain development through a word status observation model

For the accurate scientific evaluation and advancement of science, it is crucial to understand the development state of a given disciplinary domain. Existing comprehension methodologies concentrate on quantitatively analyzing broad subject trends without considering the underlying complex status attributes of the words that support and enrich these surface-level trends. Through the perspective of the word role, this study deepens the examination of domain development in a more granular way. We use Word2vec to identify the representative semantic neighbors of a domain-specific feature word from literature. Then, a word status observation model is provided that classifies the role of these representative words as tree structures, including young leaves, dead leaves, roots, and trunk-branches, based on changes in their similarity ranking divergence and information entropy. The static word role provides a new insight into detecting the internal detailed organizational composition and maturing status, while the dynamic role shift helps track historical development changes in the domain. Taking attention in psychology as a case by extracting psychology articles from Microsoft Academic Graph, the empirical study illustrates the results of the observation model and yields intriguing findings. First, the static word role positioning shows that a word's status can be obviously different. Different status indicates areas currently keeping the states of thriving, obsolete, becoming firm supportive pillars (precipitating), and irregularly developing within the domain, respectively. Second, during different development periods, most of the words are maintaining the role of irregularly changing or tending towards precipitation. A certain proportion of words are tending towards prosperity, while only a small set of words are deviating from the prosperity state. The growth of attention-related studies as a whole is well-grown and gradually approaching maturity. Our study further supports researchers’ understanding of the domain development status from a more granular perspective of word roles.
Swipe gestures for user authentication in smartphones

As more people rely on smartphones to store sensitive information, the need for robust security measures is all the more pressing. Because traditional one shot authentication methods like PINs and passwords are vulnerable to various attacks, we present a behavioral biometrics based smartphone authentication system using swipes. While previous research focused on a single kind of swipe, our data set features swipes using different fingers and directions collected from 36 users across three sessions. In our system, we experimented with support vector machine (SVM) and random forest (RF) classifiers. We investigated which finger, direction, and classifier provided the best individual swipe authentication results. Then, we analyzed whether fusion of different fingers and directions improved results. The best unimodal result came from a rightward swipe with right thumb using SVM, which resulted in an area under ROC curve (AUC) of 0.936 and an equal error rate (EER) of 0.135. We found that swipes using thumbs offered better performance. Fusion improves results for the most part, and our best result was the combination of a leftward swipe with right thumb and a leftward swipe with left thumb. This combination gave an AUC of 0.969 and EER of 0.081 with the SVM classifier.